基于域对抗的自适应环境运动目标状态检测方法OA

The biological water quality early warning systems commonly utilize fish as bio-indicators,implementing early warnings for the water quality of source water flowing through the monitoring chamber by automatically detecting the behavioral states of fish targets within the chamber.However,during prolonged operation,due to factors such as impurities,algae,and scale buildup in the source water,the accuracy of fish target detection within the monitoring chamber tends to decrease when using existing deep learning frameworks.To address this issue,this paper proposes an adaptive environmental fish target detection model,which includes a foreground fusion module,a domain adver-sarial module,and a target detection module.The foreground fusion module combines the input image with its corre-sponding binary image of the target contours as the model input.The domain adversarial module uses a domain clas-sification network with a gradient reversal layer to classify the domain of the input image.The target detection mod-ule enhances the annotation information of training data in different domains through weak supervision.Finally,we construct five different aquatic environment datasets by altering the background of the monitoring chamber and con-ducted experiments on these datasets.Experimental results show that the model can accurately classify the status of fish targets even when the environmental conditions in the monitoring chamber change.