Small-Target Detection Based on an Attention Mechanism for Apron-Monitoring Systems

Small-target detection suffers from the problems of low average precision and difficulties detecting targets from airport-surface surveillance videos. To address this challenge, this study proposes a small-target detection model based on an attention mechanism. First, a standard airport small-target dataset was established, where the absolute scale of each marked target meets the definition of a small target. Second, using the Mask Scoring R-CNN model as a baseline, an attention module was added to the feature extraction network to enhance its feature representation and improve the accuracy of its small-target detection. A multiscale feature pyramid fusion module was used to fuse more detailed shallow information according to the feature differences of diverse small targets. Finally, a more effective detection branch structure is proposed to improve detection accuracy. Experimental results verify the effectiveness of the proposed method in detecting small targets. Compared to the Mask R-CNN and Mask Scoring R-CNN models, the detection accuracy of the proposed method in two-pixel intervals with the lowest rate of small targets increased by 10%, 3.04% and 16%, 15.15%, respectively. The proposed method proved to have a higher accuracy and be more effective at small-target detection.