AD-Det: Boosting Object Detection in UAV Images with Focused Small Objects and Balanced Tail Classes
This work improves object detection for UAV imagery, which is crucial for applications like surveillance and monitoring, but it is incremental as it builds on existing methods for specific challenges.
The paper tackles object detection in UAV images by addressing scale variations and class imbalance with a novel framework, achieving a 37.5% AP on VisDrone and outperforming competitors by at least 3.1%.
Object detection in Unmanned Aerial Vehicle (UAV) images poses significant challenges due to complex scale variations and class imbalance among objects. Existing methods often address these challenges separately, overlooking the intricate nature of UAV images and the potential synergy between them. In response, this paper proposes AD-Det, a novel framework employing a coherent coarse-to-fine strategy that seamlessly integrates two pivotal components: Adaptive Small Object Enhancement (ASOE) and Dynamic Class-balanced Copy-paste (DCC). ASOE utilizes a high-resolution feature map to identify and cluster regions containing small objects. These regions are subsequently enlarged and processed by a fine-grained detector. On the other hand, DCC conducts object-level resampling by dynamically pasting tail classes around the cluster centers obtained by ASOE, main-taining a dynamic memory bank for each tail class. This approach enables AD-Det to not only extract regions with small objects for precise detection but also dynamically perform reasonable resampling for tail-class objects. Consequently, AD-Det enhances the overall detection performance by addressing the challenges of scale variations and class imbalance in UAV images through a synergistic and adaptive framework. We extensively evaluate our approach on two public datasets, i.e., VisDrone and UAVDT, and demonstrate that AD-Det significantly outperforms existing competitive alternatives. Notably, AD-Det achieves a 37.5% Average Precision (AP) on the VisDrone dataset, surpassing its counterparts by at least 3.1%.