Research on the application of lightweight YOLO model in detection of small crop diseases and pests
In response to the problem of insufficient accuracy in early small change target recognition in crop pest detection,a lightweight plant pest detection algorithm YOLO-MobileNet-CBAM is proposed.This algorithm replaces the backbone extraction network of YOLOv5s with a lightweight convolutional module of MobileNetV3 to reduce parameter computation,and introduces CBAM attention mechanism to strengthen important feature extraction from both channel and spatial dimensions,effectively enhancing the detection accuracy of small targets.It improves training speed and avoids gradient vanishing problems by replacing the original model's SiLU activation function with H-SiLU in the convolutional module.The prediction box regression loss function utilizes the SIoU function instead of the GIoU function in the original model,accounting for shape loss to further improve accuracy of small target localization.Finally,four detection heads with varying scales are output via the feature pyramid to identify large-scale diseases,small diseases and pest targets,thereby enhancing the detection accuracy of small targets.The results show that YOLO-MobileNet-CBAM achieves an accuracy rate of 92.38%,a recall rate of 90.24%,and an average accuracy of over 90%in detecting small pests and diseases.It achieves lightweight model design while effectively improving detection accuracy,and provides technical support for handheld terminal detection applications.
cropssmall diseases and pestslightweight modelYOLO-MobileNet-CBAM
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