Arthropod Object Detection Model Based on Improved YOLOv4-tiny
Aiming at the situation that the model detection efficiency is not high,and the bounding box prediction is wrong caused by the complex background,variety of morphology,occlusion target and diverse target scale of arthropods in the natural environment,an arthropod target detection model based on improved YOLOv4-tiny is proposed.Firstly,combining spatial and channel convolutional attention mechanism(CBAM),the background noise is suppressed.Secondly,deformable convolution(DCN)and an improved weighted bidirectional feature pyramid are introduced to reshape the convolution and feature fusion methods for multiscale prediction.Finally,a layer of Feat@3 is extracted in the FPN network,and a spatial pyramid pool structure is embedded to effectively extract various significant features of arthropods,so as to enhance the generalization ability of the model.The improved model is named YOLOv4-tiny-ATO.The experimental results show that the proposed model balances detection speed and accuracy well with a size of only54.6 Mb.The detection accuracy is 0.725,the detection speed reaches 89.6 frames per second,and the recall rate reaches 0.585,which is 0.426 higher than that of the YOLOv4-tiny model before the improvement.The model is more suitable for mobile deployment in terms of model size and detection speed,and the model detection accuracy can also meet the application standards to meet the detection needs of arthropods.
国家科技期刊平台
NSTL
万方数据
维普
