Mshf-Yolo:Cotton Growth Detection Algorithm Integrated Multi-Semantic and High-Frequency Features

Abstract

Cotton growth monitoring is a crucial method for achieving precise management of cotton. Currently, deeplearning-based object detection models have been widely applied in cotton detection. However, existingmodels often struggle to achieve good detection results when facing challenges such as complex environments,occlusion and low contrast. To address these issues, this paper proposes a precise MSHF-YOLO based on theYOLOv8 algorithm. In this network, we introduce Multi-Semantic Spatial and Channel Attention (MSCA)into the backbone, and the upsampling and downsampling modules in the network’s neck are replacedwith the DySample and Adaptive Wavelet Down (AWD) modules to reduce the loss of high-frequencyfeatures. Finally, to further emphasize high-frequency features, we add a high-frequency boost module tothe detection head. The test results show that the algorithm achieves mAP@0.5 and mAP@0.75 of 86.0% and68.2%, respectively, which represents an improvement of 5.5% and 3.5% compared to the original algorithm.Additionally, the model size is reduced by 12.5%. Therefore, this algorithm shows great potential for cottongrowth monitoring applications.

Keywords: Cotton growth, YOLO, Wavelet transform, Image recognition