OLCN: An Optimized Low Coupling Network for Small Objects Detection
Yuan Yuan, Yuanlin Zhang · IEEE Geoscience and Remote Sensing Letters · 2021
In remotely sensed images, it is quite common to run into small objects, such as cars and small storage tanks. However, these small objects are quite easy to get ignored because of the positioning difficulty. Thus, small objects detection is very challenging for the remote sensing object detection task. In order to deal with this challenge, theoptimized low coupling network(OLCN) is proposed. First, alow coupling robust regression(LCRR) module improves the positioning accuracy to avoid small objects getting missed. Second, areceptive field optimizing layer(RFOL) is proposed to train better classifiers by providing more accurateregions of interest(RoIs). Experimental results on the public dataset HRRSD verify the effectiveness of the proposed OLCN. Small objects detection metric is improved from 5.70% of the baseline to 22.90% of the OLCN. Moreover, the proposed method has reached state-of-the-art performance on the HRRSD dataset.