Lightweight detection model of citrus leaf disease based on improved SSD
Aiming at the problems of large model proportion,slow detection speed,and low accuracy in the current target detection algorithm for citrus leaf disease,a lightweight detection method based on improved single shot multi-box detector(SSD)for citrus leaf disease was proposed.MobileNetV2,a lightweight convolutional neural network,was introduced as the backbone of the SSD network to reduce the model size and improve the detection speed.The RFB(receptive field block)was introduced into the shallow prediction feature map to expand its receptive field,so as to improve the detection effect of the model on small targets.Additionally,the coordinate attention(CA)was in-troduced to strengthen feature information at different depths,further enhancing the recognition accuracy of citrus leaf disease.The results showed that compared with the VGG16-SSD network,the improved model(MR-CA-SSD)a-chieved an mean average precision(mAP)increase of 4.4 percentage points in citrus leaf disease detection,reduced the model proportion by 52.3 MB,and improved the frames per seconds by 3.15.The comprehensive performance of MR-CA-SSD also outperformed algorithms such as YOLOv4,CenterNet,and Efficientnet-YoloV3.This improved model could achieve rapid and accurate diagnosis of citrus leaf disease,contributing to timely and precise pesticide application for diseased areas.
citrusleaf diseaselightweight networkreceptive field blockattention mechanism
万方数据
维普
