A Cluster Chip Selection Method for Object Detection in Aerial Images

Yungi Ha, Changha Lee, Chan-Hyun Youn

Abstract: Although several natural image datasets provide promising detection results, the performance of state-of-the-art detectors on aerial images is unsatisfactory in both accuracy and efficiency. To enhance the detection performance for aerial images, clustered object detection was introduced. It enhanced detection performance by identifying areas where objects are dense, which we call them as cluster chips, and applying fine detectors to those areas to combine them with global detection results. Although it demonstrated enhanced detection performance, still there is a room for improvement in terms of cluster chip selection. In this work, we propose a cluster chip selection scheme which identifies the cluster chip with greater performance improvement. We demonstrate our proposed method through comparison with other methods in terms of detection performance(e.g. mAP, mAP50 and mAP75). Experimental results show that the proposed method has performance gain over baseline methods in mAP, mAP50 and mAP75.