Small object detection via dual inspection mechanism for UAV visual images

摘要

Unmanned Aerial Vehicles (UAVs) are utilized instead of humans to complete aerial assignments in various fields. With the development of computer vision, object detection has become one of the core technologies in UAV application. However, object detection of small targets often has missed detection, and the detection performance is far less than that of large targets. In this paper, we propose a dual inspection mechanism, which identifies missed targets in suspicious areas to assist single-stage detection branches, and shares dual decisions to make feature-level multi-instance detection modules produce reliable results. Firstly, the detection results contain missed targets is confirmed, which are in the part that does not reach the confidence threshold. For this reason, the feature vector provided by the denoising sparse autoencoder is calculated, and this part of the result is filtered again. Secondly, we empirically reveal that single detection results are not reliable enough, and the multiple attributes of the target need to be considered. Motivated by this, the initial and secondary detection results are combined and rank by importance. Finally, we give the corresponding confidence to the top-ranked instance, making it possible to become the object again. Experimental results reflect that our mechanism improves 2.7% mAP on the VisDrone2020 dataset, 1.0% mAP on the UAVDT dataset and 1.8% mAP on the MS COCO dataset. We propose detection mechanism which achieves state-of-the-art levels on these datasets and it performs better on small object detection.