A Lightweight Algorithm for Detecting Abnormal Interactive Behaviors of Bid Evaluation Personnel Based on Improved YOLOv11n

The intelligent monitoring and management of bid evaluation centers are crucial for enhancing the supervision intensity and level of bidding and bid evaluation work. With the increase in the number of bid evaluation projects and the growing diversification of bid evaluation methods, the introduction of advanced artificial intelligence detection technology has become imperative. A lightweight algorithm is proposed in this study for detecting abnormal interactive behaviors of bid evaluation personnel. The algorithm, derived from an improved version of YOLOv11n, replaces the original model's backbone network with EfficientNet to reduce network scale. Additionally, the model incorporates the LSKA attention module and BiFPN net to enhance detection capacity. Experimental results demonstrate significant improvements in detection accuracy: precision increases by 3.2%, mAP by 1.2%, and F1-score by 1.6%. The improved YOLOv11n model is 3.9 MB in size, 63.9% smaller than the original, with a faster detection time per image by 0.2 ms and reduced FLOPs by 2.4G. These results indicate the model's strong detection capabilities and lightweight advantages, offering potential for efficient bid evaluation supervision support.