Abstract
For facing of the problems caused by the YOLOv4 algorithm’s insensitivity to small objects and low detection precision in traffic light detection and recognition, the Improved YOLOv4 algorithm is investigated in the paper using the shallow feature enhancement mechanism and the bounding box uncertainty prediction mechanism. The shallow feature enhancement mechanism is used to extract features from the network and improve the network’s ability to locate small objects and color resolution by merging two shallow features at different stages with the high-level semantic features obtained after two rounds of upsampling. Uncertainty is introduced in the bounding box prediction mechanism to improve the reliability of the prediction of the bounding box by modeling the output coordinates of the prediction bounding box and adding the Gaussian model to calculate the uncertainty of the coordinate information. The LISA traffic light data set is used to perform detection and recognition experiments separately. The Improved YOLOv4 algorithm is shown to have a high effectiveness in enhancing the detection and recognition precision of traffic lights. In the detection experiment, the area under the PR curve value of the Improved YOLOv4 algorithm is found to be 97.58%, which represents an increase of 7.09% in comparison to the 90.49% score gained in the Vision for Intelligent Vehicles and Applications Challenge Competition. In the recognition experiment, the mean average precision of the Improved YOLOv4 algorithm is 82.15%, which is 2.86% higher than that of the original YOLOv4 algorithm. The Improved YOLOv4 algorithm shows remarkable advantages as a robust and practical method for use in the real-time detection and recognition of traffic signal lights.
Highlights
The Principle of YOLOv4 AlgorithmThe YOLOv4 algorithm divides the network input into S × S grid units; each grid unit predicts B bounding boxes, the bounding box confidence, and C category probabilities
For facing of the problems caused by the YOLOv4 algorithm’s insensitivity to small objects and low detection precision in traffic light detection and recognition, the Improved YOLOv4 algorithm is investigated in the paper using the shallow feature enhancement mechanism and the bounding box uncertainty prediction mechanism
In order to verify the performance of the Improved YOLOv4 algorithm for traffic signal detection, experiments were carried out using the LISA traffic light data set of the Intelligent and Safe Automobile Laboratory of the University of California, San Diego [21]
Summary
The YOLOv4 algorithm divides the network input into S × S grid units; each grid unit predicts B bounding boxes, the bounding box confidence, and C category probabilities. The accuracy is expressed as the intersection over union (IOU) of the predicted bounding box and the real bounding box according to Equation (1). Where confidence is the confidence of the bounding box and Pr(object) is the probability of the object being detected in the grid. The input image is divided into 19 × 19 grid units. The width and height of the entire image are widthimg and heightimg , respectively, which are divided into s × s grid units. The width and height of the bounding box are widthbox and heightbox , respectively. (1) Boundary box width and height normalization according to Equations (2) and (3), respectively. (2) Center point coordinate normalization according to Equations (4) and (5)
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