Improved Light-Weight Target Detection Method Based on YOLOv5

Abstract

Aiming at the problems of the YOLOv5, such as large model size, enormous amount of computation, and low accuracy of target box regression, a new model with fewer parameters, less computation, faster convergence speed, and higher accuracy was proposed. Firstly, the improved SKConv was used to greatly reduce the number of model parameters and increase the receptive field range of the network. Secondly, C-ECA, an optimized version of the channel attention module ECA, was added to the model to obtain attention information in a cross-channel way, so that the model could more accurately focus on important features among complex features. Then, the MSM structure is designed to effectively improve the feature extraction ability of the network. Finally, the backbone network is deepened to extract more intermediate features, and the depth of the feature pyramid and detection layer are increased accordingly so that the network can make full use of intermediate features and accurately detect more targets. The experimental results show that compared with YOLOv5s, the number of parameters of the final models is reduced by 18.6%, and the calculation amount is reduced by 8.1%, and when tested on the VOC2007 test dataset, mAP@0.5 and mAP@0.5:0.95 improved by 3.3 percentage points and 5.6 percentage points, respectively. The improved model has significantly improved the ability of target detection in various complex environments and realizes the target detection model with low parameters and high accuracy, which provides important ideas for the design of lightweight target detection networks.