TEANS: A Target Enhancement and Attenuated Nonmaximum Suppression Object Detector for Remote Sensing Images

Hai-Bao Chen, Shan Jiang, Guanghui He, Bingyi Zhang, Hao Yu

Abstract: In this letter, we propose an effective approach to learn a convolutional neural network (CNN) model with target enhancement and attenuated nonmaximum suppression (NMS) technique (TEANS) for object detection in optical remote sensing images. TEANS mainly consists of two steps. First, the target enhancement architecture, including target upsampling and reconvolution, is designed into a given deep ResNet-101 model for accurate object detection, especially for small ones. Second, the attenuated NMS technique is used for overcoming wrong eliminations of serried object proposals. For verifying the effectiveness of the TEANS method, evaluations are implemented on a publicly available 15-class optical remote sensing object detection data set. Experimental results show that TEANS can achieve 5.55%, 18.77%, 26.81%, 55.07%, 28.48%, 6.01%, and 5.51% improvements in mean Average Precision (mAP), respectively, compared with standard Faster R-CNN, R-FCN, YOLOv2, SSD, USB-BBR, YOLOv3, and MS-VANs frameworks.