Research on Object Detection Algorithm Based on Twin Attention and Adaptive Feature Fusion

Object detection as a core task in computer vision faces enormous challenges, especially when dealing with small and dense objects. In order to solve this problem, this paper proposes an object detection algorithm based on the fusion of twin attention and adaptive features, which aims to improve the accuracy and stability of detection. The design of the twin attention mechanism can accurately capture the subtle changes in the object area and its surrounding background, significantly enhancing the algorithm's perception of the object, so that the submerged or occluded object can be effectively identified in a complex background. At the same time, the adaptive feature fusion technology introduced in this paper abandons the traditional simple splicing method, but dynamically adjusts the fusion strategy according to the importance of the feature map, and realizes the optimal utilization of different levels of feature information. This innovation makes the algorithm more intelligent and efficient in feature extraction and utilization, and further improves the accuracy and robustness of object detection. The experimental results on the PASCAL_VOC2007 data set show that the proposed algorithm achieves a significant improvement of 5% in average precision(mAP) compared with the benchmark YOLOv8 algorithm. This significant performance improvement not only proves the superiority of the proposed algorithm in object detection tasks, but also provides strong technical support for promoting the improvement of object detection performance in practical applications.