Lightweight target detection algorithm based on improved YOLOv4

Abstract

Traditional YOLOv4 object detection network is difficult to be applied to mobile embedded devices because it has some deficiencies such as complex structure and too many parameters. In this paper, the authors propose a lightweight object detection algorithm. Firstly, Mobienetv3 is used to replace the original feature extraction network, and the Mish function is used to be the activation function, which reduces the number of model parameters. Secondly, the dilated convolution is used to replace the maximum pooling operation in the original spatial pyramid pooling (SPP) structure. Then, a custom Dcn-Dw structure is used to replace the convolution operation in the original PANet, which improves the accuracy of the model for irregular object detection and reduces the model size. Finally, a CBAM lightweight attention mechanism module is introduced in front of the YOLO Head, which further improves the model accuracy. An experiment on the VOC2007 dataset is carried out, and the results show that the mean average precision (mAP) is 80.3% and frames per second (FPS) is 15. At the expense of 3% accuracy, the detection speed is increased to two to three times, and the model size is reduced to one-fifth of the original model. The lightweight object detection algorithm can suit for real-time detection tasks on resource-constrained embedded devices.