To improve the accuracy of the vehicle detection algorithm,an algorithm based on the YOLOv5 framework with the addition of a novel lightweight attention module(NLAM)and a multi-scale feature detection layer was proposed.The NLAM module was a parallel fusion of the spatial attention module with depth-separable convolution and the channel attention module with one-dimensional convolution,which made the number of parameters of the NLAM module only 8.The multi-scale feature detection layer was added to improve the detection accuracy of small targets.The algorithm was trained and tested on the KITTI dataset.Experimental results show that the average accuracy of the improved algorithm is 89.9％,which is 2％higher than that of the original algorithm,and the detection frame rate is 90 frames/s.The algorithm has higher small target detection accuracy and better robustness for vehicle detection.
关键词
深度学习/目标检测/注意力模块/新型轻量化/多尺度特征/车辆检测/YOLOv5s算法
Key words
deep learning/target detection/attentional module/novel lightweight/multiscale features/vehicle detection/YOLOv5s algorithm
维普
万方数据