Abstract:

In order to improve the effectiveness of detecting illegal vehicles in traffic monitoring videos， a multi-objective detection method for illegal vehicles in traffic monitoring videos based on the YOLOv5 network algorithm is proposed. In the fusion processing of traffic monitoring video images， the grayscale histogram equalization operation is performed， and the grayscale deviation values of different exposed images are calculated through the window function. All ghosts in the fused images are removed by image corrosion， and high-quality image fusion results are obtained through pixel normalization and Laplace pyramid； Based on the YOLOv5 network algorithm， a multi head self attention learning mechanism is constructed using a driver graph encoder in the Transformer module to enhance the semantic information of target features of illegal vehicles in the image； On the basis of continuous dilated convolution， dense connection structures are utilized to expand the feature map convolution into a single pixel addition operation， enhancing the semantic information of features； Utilizing the Softmax function for feature multi-scale fusion to achieve multi-target detection of illegal vehicles in traffic surveillance videos. The experimental results show that the proposed method can effectively improve the quality of traffic monitoring video images， and the YOLOv5 network algorithm used has high computational intensity， resulting in more accurate multi-target detection of illegal vehicles.

Key words: YOLOv5 network algorithm, traffic monitoring videos, violating vehicles, multi object detection