CA2Det: Cascaded Adaptive Fusion Pyramid Network Based on Attention Mechanism for Small Object Detection

Abstract

How to achieve fast and accurate small object detection holds crucial theoretical and practical significance. However, this task encounters substantial challenges due to scale differences among instances in the scene, along with the scarcity of inherent features and weak representation of small instances. To alleviate the above problems, we propose a novel attention-based cascaded adaptive feature pyramid fusion network, CA2Det, to effectively improve the small object detection performance. First, to prevent the information degradation of small-instance features during training, we introduce the efficient and lightweight Shuffle Attention mechanism to highlight the features of small instances. Second, to mitigate the information conflicts arising from the scale inconsistency among instances, we design a double-layer cascaded adaptive fusion pyramid module, CAFP, which can effectively suppress the information conflicts while enabling full information exchange across layers. Finally, we combine sparse convolution to achieve efficient high-resolution input, providing richer geometric information of the instances. Compared to the baseline network, on the COCO benchmark dataset and the popular UAV dataset VisDrone, both of which contain a large number of small instances, the proposed method improves the detection accuracy mAP values by 1.1% and 2.2%, respectively, while having a good real-time detection speed.

Keywords

• Small object detection
• feature fusion
• attention mechanism
• multi-scale detection
• deep learning