Car Color Recognition
This example takes an image as input, detects the cars using YOLOv4 object detector, crops the car images, resizes them to the input size of the classifier, and recognizes the color of each car. The result is shown on the display and saved as output.jpg image file. $ python car_color_classifier.
Use AI to identify the color of a car. Great for Vehicle Sales Analytics, Insurance Risk Assessment, Car Manufacturing Marketing and much more. Built with Nyckel, an API for building classification models at scale.
Vehicle recognition results with 24 colors | Download Scientific Diagram
One such application is car detection and color identification, which has broad use cases in traffic analysis, parking management, and automotive inventory systems. 1800 open source cars color images and annotations in multiple formats for training computer vision models. Cars color recognition (v1, 2023-09-21 12:57pm), created by Final Project.
Abstract-Vehicle color information is one of the important elements in ITS (Intelligent Traffic System). In this paper, we present a vehicle color recognition method using convolutional neural network (CNN). Naturally, CNN is designed to learn classification method based on shape information, but we proved that CNN can also learn classification based on color distribution.
Some samples of vehicle color recognition on videos. | Download Scientific Diagram
In our method, we. Dataset Used The Vehicle Color Recognition Dataset contains 15601 vehicle images in eight colors, which are black, blue, cyan, gray, green, red, white and yellow. CarNET API provides you with the ability to detect a car's make, model, generation, color and angle from an image of the car.
Our API is powered by computer vision and deep learning technologies, and is capable of correctly recognizing cars in different lighting and weather conditions. However, vehicle color recognition is greatly affected by the background, light, and weather, which brings a challenge on how to improve the robustness of vehicle color recognition system. In this paper, we propose a new robust real-time vehicle color recognition system based on the YOLO9000 object detection [9].
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Vehicle color is one of the significant and stable characteristics of vehicles, and plays an important role in intelligent transportation system. On the basis of deep VGG-16 model, a vehicle color recognition network model based on deep convolution neural network is formed by adjusting network structure and optimizing network parameters. This method can recognize common vehicle color quickly.
Color, as a notable and stable attribute of vehicles, can serve as a useful and reliable cue in a variety of applications in intelligent transportation systems. Therefore, vehicle color recognition in natural scenes has become an important research topic in this area. In this paper, we propose a deep.
