Self-calibrated YOLOv5 Detection Enhancement Algorithm for Small Targets
In order to solve the problems of low precision of intelligent driving and missing and false detection of small targets,a road target detection algorithm based on self-calibrated convolutional YOLOv5s is proposed.In this algorithm,a self-calibrated convolutions network is designed to improve the detection accuracy and detection ability of small targets through deep feature extraction and feature fusion.Lightweight processing is applied to the self-calibrated convolutions network to reduce model size and the number of parameters during training.A small target calibration detection layer is added to output feature maps for detecting small objects.Additionally,the SAIoU loss function is designed to replace the CIoU loss function in target box regression,accelerating the regression of target boxes.The proposed algorithm was tested on KITTI and BDD100K publicly available autopilot datasets,and the mean average precision of detection can reach 95.1%and 53.1%,respectively.Compared to the YOLOv5s algorithm,it improves by 2.1 and 4.2 percentage points on these datasets,respectively.Comparative experiments with other algorithms demonstrate certain advantages in detection accuracy and small targets detection capability.Moreover,the lightweight self-calibrated convolutions network compresses the model size by 35%compared to its non-lightweight,enhancing real-time performance.These results indicate that the proposed algorithm meets real-time requirements and can improve detection accuracy and small targets detection capability.
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