YOLM: A Remote Sensing Aircraft Detection Model
Wei Liu, Jinwen Tian, Tian Tian · IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium · 2022
Aircraft detection in remote sensing images plays an essential role in military and civil applications. Many aircraft in remote sensing images are small targets and are covered by cloud and fog, which make it difficult to extract sufficient feature information for aircraft detection. In this paper, a single-stage object detection model YOLM (You can Look More) has achieved better remote sensing aircraft detection performance by extracting more features. YOLM includes an enhanced neck network with a four-layer feature pyramid and a path aggregation network. More layers enable the model to extract more detailed information. To use the background information around the aircraft as a supplement to aircraft features, an attention module which can obtain the image context information of aircraft is designed. Experiments are conducted on the aircraft subset of FAIR1M data set and some aircraft images we obtained, and the proposed model has out-performed many baseline methods such as YOLO V5s.