Simple　Summary　This　research　proposes　a　novel　attention　mechanism　for　the　task　of　rice　pest　detection,　aiming　to　address　the　issues　of　complex　backgrounds　and　small　size　of　pests.　By　dynamically　adjusting　attention　weights,　the　model　effectively　focuses　on　small-scale　pests,　avoiding　distractions　from　complex　background　information.　Concurrently,　we　adopt　a　multi-scale　feature　fusion　technique,　successfully　extracting　rich　and　distinctive　features,　thereby　further　enhancing　the　model＇s　performance.　Numerous　experiments　demonstrate　superior　performance　of　our　model　over　advanced　methods　like　YOLO,　EfficientDet,　RetinaDet,　and　MobileNet　in　pest　detection　tasks.　Overall,　through　innovative　attention　mechanism　and　feature　fusion　techniques,　our　work　effectively　tackles　the　critical　issues　in　pest　detection,　achieving　excellent　detection　results.　In　this　work,　an　attention-mechanism-enhanced　method　based　on　a　single-stage　object　detection　model　was　proposed　and　implemented　for　the　problem　of　rice　pest　detection.　A　multi-scale　feature　fusion　network　was　first　constructed　to　improve　the　model＇s　predictive　accuracy　when　dealing　with　pests　of　different　scales.　Attention　mechanisms　were　then　introduced　to　enable　the　model　to　focus　more　on　the　pest　areas　in　the　images,　significantly　enhancing　the　model＇s　performance.　Additionally,　a　small　knowledge　distillation　network　was　designed　for　edge　computing　scenarios,　achieving　a　high　inference　speed　while　maintaining　a　high　accuracy.　Experimental　verification　on　the　IDADP　dataset　shows　that　the　model　outperforms　current　state-of-the-art　object　detection　models　in　terms　of　precision,　recall,　accuracy,　mAP,　and　FPS.　Specifically,　a　mAP　of　87.5%　and　an　FPS　value　of　56　were　achieved,　significantly　outperforming　other　comparative　models.　These　results　sufficiently　demonstrate　the　effectiveness　and　superiority　of　the　proposed　method.