Multiscale Feature Enhancement and Adaptive Receptive Field for Tiny Object Detection in Remote Sensing Images

In the field of remote sensing, detecting tiny objects remains a significant challenge, essentially due to the insufficient and weak feature representations caused by the limited pixel coverage of these objects, which presents a pressing demand for networks possessing multiscale feature enhancement ability. Furthermore, the unique prior knowledge presented in remote sensing images is not utilized effectively or appropriately, making this information just an obstacle to detecting tiny objects. To address these issues, we propose an effective network for tiny object detection called Multiscale Feature Enhancement and Adaptive Receptive Field YOLO (MA-YOLO). Specifically, the MA-YOLO integrates two innovative plug-and-play modules: the Multiscale Feature Enhancement Module (MFEM) and the Receptive Field Adaptive Multiscale Feature Fusion Module (RFAMFFM). The MFEM is designed to extract multiscale and multi-geometric features through a multi-branch refinement design with multi-size and -shape convolution, thereby enhancing feature representation. Driven by the larger receptive fields of large-kernel convolution and the guiding property of global pooling, the RFAMFFM is employed to dynamically adjust and select the receptive field to leverage prior knowledge effectively. Our proposed network outperforms the existing comparison models on two publicly available datasets, i.e., USOD and VEDAI. Specifically, compared to the state-of-the-art model, MA-YOLO achieves significant improvements of 2.2% on the USOD dataset and of 1.8% on the VEDAI dataset for the mAP50:95 metric.