Cotton leaf disease detection method based on improved SSD

In response to the problems of numerous model parameters and low detection accuracy in SSD-based cotton leaf disease detection methods, a cotton leaf disease detection method based on improved SSD was proposed by combining the characteristics of cotton leaf diseases. First, the lightweight network MobileNetV2 was introduced to improve the backbone feature extraction network, which provides more abundant semantic information and details while significantly reducing the amount of model parameters and computing complexity, and accelerates the detection speed to achieve real-time detection. Then, the SE attention mechanism, ECA attention mechanism, and CBAM attention mechanism were fused to filter out disease target features and effectively suppress the feature information of jamming targets, generating feature maps with strong semantics and precise location information. The test results on the self-built cotton leaf disease dataset show that the parameter quantity of the SSD_MobileNetV2 model with backbone network of MobileNetV2 was 50.9% of the SSD_VGG model taking VGG as the backbone. Compared with SSD_VGG model, the P, R, F1 values, and mAP of the MobileNetV2 model increased by 4.37%, 3.3%, 3.8%, and 8.79% respectively, while FPS increased by 22.5 frames/s. The SE, ECA, and CBAM attention mechanisms were introduced into the SSD_VGG model and SSD_MobileNetV2 model. Using gradient weighted class activation mapping algorithm to explain the model detection process and visually compare the detection results of each model. The results indicate that the P, R, F1 values, mAP and FPS of the SSD_MobileNetV2+ECA model were higher than other models that introduced the attention mechanisms. Moreover, this model has less parameter with faster running speed, and is more suitable for detecting cotton diseases in complex environments, showing the best detection effect. Therefore, the improved SSD_MobileNetV2+ECA model significantly enhanced the semantic information of the shallow feature map of the model, and has a good detection effect on cotton leaf diseases in complex environments. The research can provide a lightweight, real-time, and accurate solution for detecting of cotton diseases in complex environments.