Automated detection of cotton aphids (Aphis gossypii Glover) using a bi-enhanced attention mechanism and domain-adaptive transfer learning strategy

Abstract Cotton aphid ( Aphis gossypii Glover) severely impacts cotton yield and quality, necessitating effective detection and control measures. Traditional manual detection methods are inefficient, highlighting the need for rapid and accurate detection. In order to realize rapid and accurate cotton aphid detection, we proposed a novel attention mechanism named Bi-Enhanced Attention Mechanism (BEAM), aiming at improving the performance of the YOLOv8-s model. Furthermore, we employed a domain-adaptive transfer learning strategy by pre-training our enhanced network model on a public forestry pest dataset and fine-tuning it on a custom-built cotton aphid dataset. In this study, we evaluate our approach using the mean Average Precision (mAP) at different Intersection over Union (IoU) thresholds. Experimental results demonstrated that our approach achieved excellent performance in detecting cotton aphids. Specifically, our approach, which includes an enhanced YOLOv8-s model with a bi-enhanced attention mechanism and a domain adaptive transfer learning strategy, achieved an mAP of 58.1% at an IoU threshold range of 0.5 to 0.95 ([email protected]:0.95), 95.4% at an IoU threshold of 0.5 ([email protected]), and 64.8% at an IoU threshold of 0.75 ([email protected]). Compared to the baseline method, which utilizes the original YOLOv8-s model with standard training procedures, there was an improvement of 4% in [email protected]:0.95, 1.3% in [email protected], and 8.1% in [email protected]. This research introduces a novel method for accurate detection of cotton aphids in the field, which is crucial for effective pest management and timely intervention.