Research on Water Garbage Detection Algorithm Based on GFL Network

Abstract

Aiming at the problem that existing water surface garbage detection algorithms cannot meet the tradeoff between real-time and accuracy in natural scenes, we propose a GFL (Generalized Focal Loss) based GFL_ HAM algorithm to improve the accuracy of water surface garbage detection, and better apply to water surface garbage detection application scenarios. We have added the HAM (Hybrid Attention Model) module to the feature extraction module (resnet50) of the GFL network to improve the feature extraction capability of the backbone network and the network representation capability. And use the self-constructed garbage detection datasets under natural scenes for training and testing. After analyzing the training process and test results, our network has higher reliability compared to the current mainstream single stage network. Our network can achieve 60.12% mAP on the water surface garbage detection datasets, which is 1.09%, 1.36%, and 1.61% higher than YOLOv3, SSD, and GFL, respectively.