Car Detection and AI Classifiers for Automated Image Editing Solutions

Automotive photography is essential for dealerships in the 21st century. Around 69% of buyers find good car visuals critical while searching for vehicle options online, while 26% consider them moderately important. Additionally, vehicle photography with traditional (manual) methods is slow, tedious, and expensive. That’s where the power of AI (artificial intelligence) can do wonders for you, by automating a portion of the task and guiding you through the rest. The first step to that is car detection. Once the AI performs that, image editing becomes a breeze!

Let’s understand what car object detection is in the context of automated image editing and what happens after.

 

What is Car Detection?

Car detection in an image is done with AI. Wherein the latter can tell if the object in the image is a car or not, without any human intervention. Therefore, the machine is trained with a dataset, it contains images of vehicles and non-vehicle objects. It is shown plenty of images and told which object is a car and which is not until it can detect the same on its own with high accuracy.

 

 

Car object detection helps edit automobile images smoothly and seamlessly. Once the AI recognizes a vehicle, it can easily remove and replace the original background with a new, custom backdrop. Additionally, it can also work on the car – tinting windows and removing their reflections, correcting its tilt, checking if the car is clean, etc. You can also train the machine with other datasets, like the vehicle classification dataset, to further improve its detection and classification accuracy. Thus, the AI would be able to recognize even the car segment!

 

 

What are the Types of Vehicle Detection Methods?

Car detection helps to keep track of different kinds of vehicles. It identifies the edges, shapes, and texture of a particular image. Therefore, it detects objects by using sensors physically embedded in the ground or using cameras and clever software.

Traditional Methods

Traditional vehicle detection methods rely on algorithms to car identifier AI in images or videos without the deep learning techniques that are popular today. Additionally, they are generally less accurate and struggle with complex scenarios like varying lighting, a car being partially hidden, and different car shapes and sizes. Here are a few categories of traditional methods that are mentioned below:

1. Background Subtraction: This traditional approach assumes a static background image and the system AI vehicle recognition changes occurring in frames that which object could be a car. It surely works well in a controlled environment. However, it struggles when there is a change in lighting and slow-moving vehicles.

2. Frame Differencing: Frame differencing compares continuous video frames and identifies significant pixel value changes, potentially indicating a moving object like a car. However, it is sensitive to noise and can be misled by camera movement or even shadows.

3. Feature-Based Methods: These techniques look for specific visual characteristics commonly associated with cars.

• Haar features: These are simple edge and line features that can identify rectangular car shapes.
• Histogram of Oriented Gradients (HOG): This method analyzes the distribution of direction changes in an image, it is useful for capturing car shapes and edges.

Deep Learning Approaches and Techniques

Deep learning has revolutionized vehicle detection. Additionally, it ensures achieving the highest accuracy if we compare it to our traditional methods. Therefore, developers can create systems accurately, well-suited, and efficiently if they learn the working of deep learning approaches.

Convolutional Neural Networks (CNNs)

CNNs are artificial neural networks that are specifically designed to process visual data like images. Additionally, they are proficient in extracting features from images, like their edges, shapes, and textures, that are crucial for car recognizers.

 

 

Here are some desired CNN builders for vehicle detection mentioned below:

1. YOLO (You Only Look Once): YOLO is a motion detector for cars that is known for its speed and is a single-stage detector. YOLO predicts bounding boxes and probabilities for objects in a single pass through the network, which makes it ideal for a real-time car detection app. Spyne.ai uses YOLO and brings changes as per the client’s requirements.

2. SSD (Single Shot Multibox Detector): SSD is also a single-stage detector. SSD offers a good balance of speed and accuracy.

3. Region-based CNNs (R-CNNs): RCNNs are two-stage detectors that first propose candidate regions where a car might be present and then use a CNN to classify those regions and refine bounding boxes. They are slower than single-stage detectors, and R-CNNs can achieve higher accuracy.
4. Faster R-CNN is built upon R-CNN models by introducing a technique called Region Proposal Network (RPN) that efficiently generates high-quality candidate regions and is swift as compared to traditional R-CNN.

5. Other Deep Learning Techniques: Transfer Learning: They are pre-trained models and can detect massive car image recognition datasets like ImageNet can be fine-tuned for 3D car detection tasks. The power of pre-learned features reduces training time on car-specific datasets.

6. Data Augmentation: It brings artificially manipulating training data with variations in lighting, scale, and occlusions and helps CNN to generalize better to real-world scenarios with diverse car appearances.

Choosing the Right Deep Learning Approach: The choice between different deep learning approaches depends on the specific application requirements.

• Accuracy vs Speed: If real-time processing is crucial, a faster single-stage detector like YOLO might be preferred. However, for tasks demanding higher accuracy, a two-stage R-CNN like Faster R-CNN might be a better choice.
• Computational Resources: Training and running complex deep-learning models require significant computational power. Therefore, if resources are limited, a simpler model or transfer learning might be necessary.
• Data Availability: Deep learning models depend on large amounts of data training. The availability of AI car image processing datasets can influence the choice of approach.

 

Car Detection Model of Spyne

Spyne’s car detection image processing model takes things further. Our AI-powered editing platforms include a web browser application — named Darkroom — and a smartphone app for iOS and Android. Both Darkroom and the smartphone app offer automated image editing, with the latter additionally offering AI-guided photoshoots.

 

Any image you upload on Spyne is checked by 35+ individual APIs to give you the best image for your digital car catalogs. Additionally, depending on your requirements, you can use our platform as is or individual APIs. You can also use our software development kit to build your own white-label app.

Steps for Car Detection and Classification

Spyne’s AI-powered vehicle detection and classification offers several features. Let’s explore what they do:

• Car Classifier

  It performs car object detection, verifying whether the object in your image is a car or not. It is a built-in feature of Spyne’s Virtual Car Studio and is also available as a separate API.

• Car Type Classifier

  It classifies the car as per Spyne’s vehicle classification chart – sedan, SUV, hatchback, and pickup truck.

• Car Shoot Category Classifier

  This system categorizes the car image, identifying whether it’s an interior shot, exterior shot, or something else entirely.

• Car Shoot Interior Classifier

  It detects the different interior features, such as an odometer or dashboard, in the image.

• Tint Classifier

  It detects if the car’s windows have been tinted or not.

• Window Masking

  Masks the car windows in the image to remove reflections.

• Window See-Through Masking

  Check if the car in the image has see-through windows.

• Doors Open

  This feature checks if the car doors are open.

• Tire Detection Classifier

  Check whether the object in the image is a tire.

• Number Plate Detection and Extraction

  This API detects the number plate of the car object.

• Number Plate Masking

  This feature masks the car’s number plate in the image, replacing it with a custom virtual plate.

• Angle Detection

  This feature detects the angle of the car relative to the camera.

• Crop Detection

  It detects if the image has been cropped.

• Distance Detection

  This API automatically detects the car’s distance in the image from the camera photographing it.

• Exposure Detection

  This feature checks the brightness of the image.

• Exposure Correction

  Performs correction of the image brightness, automatically correcting bright or dark images.

• Reflection Classifier

  Check to see if there is any reflection on the car, like of a nearby object such as a tree or electric pole.

• Reflection Correction

  This feature corrects the reflections on the car’s body in the picture.

• Car Is Clean

  This API checks if the car body is clean or has any mud on it.

• Tire Mud Classifier

  Check to see if there is mud on the car’s tires in the photograph.

• Tilt Classifier

  Analyze the image to see if the car is tilted to one side.

• Exterior Background Removal

  Removes the original background from the car’s exterior shots.

• Interior Background Removal

  Removes background from the car’s interior shots.

• Miscellaneous Background Removal

  Removes background from all other pictures of the vehicle.

• Floor Generation and Shadow Options

  Generates a virtual floor beneath the car to give the image a realistic look.

• Logo Placement

  This feature assists in placing your dealership’s logo in the image.

• Tire Reflection

  This creates a reflection of the tires on the virtual floor for a realistic look.

• Watermarks

  Check if there are any watermarks on the image.

• Watermark Removal

  Removes any watermarks from the photograph.

• Diagnose Image

  This feature checks if there is any issue with the image’s aspect ratio, size, and resolution.

• Super Resolution

  Increases image resolution as per your dealership website/marketplace requirements.

• Aspect Ratio

  This feature uses super-resolution to increase or decrease the image’s aspect ratio per your needs.

• DPI

  This feature uses super-resolution to increase or decrease the DPI (Dots Per Inch) as per your needs.

• Additional Parameter Correction

  Corrects the image’s aspect ratio, size, and resolution.

• Video Trimmer

  Generates frames from a 360° car video to create an interactive 360° spin view.

• Wipers Not Raised

  This API analyzes the image to determine if the vehicle’s wipers are raised.

• Object Obstruction

  Check if there is any object obstruction before the car leading to hampered visibility.

• Blurry, Stretched, Or Distorted Images

  This API checks if the car or the image is blurry or doesn’t adhere to the quality standards.

 

What is YOLO?

YOLO, which stands for “You Only Look Once,” is a groundbreaking object detection algorithm that has sparked a revolution in the field of computer vision. This algorithm addresses the challenging task of real-time object detection and localization in images and videos with exceptional accuracy and speed.

Traditionally, object detection algorithms involved multiple stages, such as region proposal and object classification, leading to relatively slower processing times. YOLO, introduced in its various versions (like YOLOv1, YOLOv2, YOLOv3, and later versions), took a radically different approach that significantly improved detection speed while maintaining accuracy.

How does YOLO work?

In the conventional approach, multiple regions of interest are proposed, and each is classified individually. YOLO, on the other hand, completely transforms this workflow. It frames object detection as a regression problem and uses a single neural network to predict both the object’s class and its bounding box coordinates directly from the full image.

Key Aspects of YOLO

1) Unified Detection

YOLO achieves detection in a unified manner. It divides the input image into a grid, and each grid cell predicts multiple bounding boxes and their associated class probabilities. This “you only look once” philosophy enables YOLO to process the entire image and predict object locations and classes in a single forward pass of the network.

2) Speed and Efficiency

YOLO’s single-pass approach makes it extremely fast compared to traditional methods that require multiple passes through the network. YOLO is capable of real-time object detection, even on resource-constrained devices.

3) Loss Function

YOLO uses a unique loss function that combines object localization loss, object classification loss, and confidence loss for bounding box predictions. This loss function guides the network to simultaneously optimize object localization and classification accuracy.

4) Anchor Boxes

To handle objects of varying sizes and aspect ratios, YOLO employs anchor boxes. These predefined boxes are used to predict object boundaries, and the algorithm determines which anchor box best fits the size and shape of the object being detected.

5) Feature Pyramid

YOLOv3 and later versions integrate feature pyramids that allow the algorithm to detect objects at different scales and resolutions. Additioanlly, this further improves detection accuracy for both small and large objects.

Impact and Applications

YOLO’s innovative approach has found widespread applications in fields such as autonomous driving, surveillance, medical imaging, and more. Additionally, its ability to provide accurate and real-time object detection has been instrumental in enhancing safety, efficiency, and automation across various domains.

In conclusion, YOLO (You Only Look Once) is a revolutionary object detection algorithm that has transformed computer vision. Its single-pass, unified approach to object detection, along with its speed and accuracy, has made it a cornerstone technology in modern visual perception systems.

 

How does a Vehicle Detection System Work?

This section describes the main structure of a vehicle detection system and counting system. The process follows a sequence of steps to automatically identify and locate vehicles within a given area using image or video analysis. Here’s an overview of the steps involved:

1) Data Acquisition

The car detection system initiates by capturing video data of the traffic scene using cameras, sensors, or similar monitoring devices. Therefore, this video data becomes the primary input for the vehicle detection system.

2) Road Surface Extraction and Division

After acquiring the video data, the system proceeds to extract and define the road surface area within each frame. Additionally, it segments the road surface to concentrate vehicle detection on the relevant area. Therefore, this extracted road surface is then divided into smaller sections or grids to enable efficient vehicle counting and tracking.

 

 

3) YOLOv3 Object Detection

As the heart of the vehicle detection and tracking process, the YOLOv3 deep learning object detection method comes into play. YOLOv3 (You Only Look Once version 3) is a cutting-edge algorithm specifically designed for real-time object detection. Additionally, it leverages the power of deep neural networks to accurately and swiftly detect various objects, including vehicles, within complex scenes.

YOLOv3 employs a grid-based approach, dividing the input image into a grid of cells. For each cell, the algorithm predicts bounding boxes that tightly enclose the detected objects. Additionally, YOLOv3 provides class probabilities for the predicted objects, allowing it to distinguish between different types of vehicles present in the traffic scene.

4) Vehicle Tracking and Counting

To ensure accurate vehicle counting and tracking, the system employs tracking algorithms. These algorithms use the information from YOLOv3’s detections to maintain a consistent identity for each detected vehicle across consecutive frames. Additionally, we commonly use Kalman filters or SORT (Simple Online and Real-time tracking) techniques to track vehicles’ movement.

5) Counting and Analysis

We then accurately count the tracked vehicles. They either enter or exit specific regions of interest within the scene. These regions could include entry and exit points of highway segments or designated monitoring areas. Additionally, by analyzing the trajectories and interactions of the detected and tracked vehicles, the system can provide insights into traffic flow, congestion, and other relevant metrics.

6) Output Visualization

The final step involves visually representing the results. Therefore, the system generates annotated video frames with bounding boxes encompassing detected vehicles, labels indicating their types, and trajectory paths illustrating the tracked movement of vehicles. Additionally, this output serves as a valuable resource for real-time monitoring, traffic management, and further in-depth analysis.

In conclusion, a vehicle detection and counting system incorporates a structured process that starts with video data input, followed by road surface extraction and division. The YOLOv3 deep learning object detection method identifies vehicles in highway traffic scenes. Through vehicle tracking, counting, and visualization of results, these systems contribute to efficient traffic management and informed decision-making.

 

Real-World Use Case of Car Object Detection

Spyne’s app can get studio-quality images for your digital showrooms. Therefore, the app has a guided photoshoot telling you what angle of the vehicle to capture. Additionally, the automatic validation feature checks the photo and tells if it can be edited or if you need to reshoot it.

 

 

Upload the image to the virtual studio and select what edits you want, whether it is background replacement, window tinting, Number Plate Masking, logo placement, etc. Moreover, the virtual studio uses car detection image processing to determine where the background lies in the image, how to remove and replace it, etc. After recognizing a vehicle in an image, the system can also edit the windows, numberplates, tires, etc. Therefore, through AI, you can edit images in bulk within seconds, and the system remembers your settings to give you a consistent-looking catalog throughout.

 

Benefits of Spyne AI Car Detection

 

Let’s look at the benefits of Spyne’s AI photo enhancer technology to create high-quality car identifier by image for your online car inventory:

1. It is quick: Faster turnaround time is a dream for all retailers. Moreover, AI photo editing software makes these dreams come true by being able to process hundreds of photographs in seconds.
2. It is cost-efficient: AI photo enhancers will help you save a lot of money. It not only saves the cost of the workforce to edit the pictures but can even help you save money spent on Car Photography.
3. Allows bulk editing at once: When your expectations for a professional car photoshoot collide with the disappointing background, it’s time to dive into a marathon editing session or even consider reshooting! With AI editing, this is only a matter of a few minutes.
4. Accuracy and Consistency: AI editors come to the rescue by eliminating and changing the background, changing colors, adding shadows, and meeting every other editing demand required. While the accuracy and consistency with manual editing rely on the editor, in the case of AI photo enhancers, you can get 100% accuracy without the chance of human mistakes or inconsistency in the edited photos.

 

Conclusion

Online viewers can have an attention span comparable to that of a goldfish. Without innovative car visuals, attracting their attention and converting them into buyers would be impossible. Moreover, trying to produce high-impact visuals through manual processes brings numerous bottlenecks. Spyne’s automated car photo shoot begins with car detection and ensures next-gen vehicle photography without hassles.

Still, have doubts or want to know more about how Spyne can help you automate vehicle photography? Book a demo, and we’ll give you a detailed explanation and demo.