A Lightweight Crop Pest Detection Method Based on Improved RTMDet

To address the issues of low detection accuracy and large model parameters in crop pest detection in natural scenes, this study improves the deep learning object detection model and proposes a lightweight and accurate method RTMDet++ for crop pest detection. First, the real-time object detection network RTMDet is utilized to design the pest detection model. Then, the backbone and neck structures are pruned to reduce the number of parameters and computation. Subsequently, a shortcut connection module is added to the classification and regression branches, respectively, to enhance its feature learning capability, thereby improving its accuracy. Experimental results show that, compared to the original model RTMDet, the improved model RTMDet++ reduces the number of parameters by 15.5%, the computation by 25.0%, and improves the mean average precision by 0.3% on the crop pest dataset IP102. The improved model RTMDet++ achieves a mAP of 94.1%, a precision of 92.5%, and a recall of 92.7% with 4.117M parameters and 3.130G computations, outperforming other object detection methods. The proposed model RTMDet++ achieves higher performance with fewer parameters and computations, which can be applied to crop pest detection in practice and aids in pest control research.