MERS-Net: A Lightweight and Efficient Remote Sensing Image Object Detector

With the rapid advancement of deep learning technology, the problem of object detection in the field of remote sensing has received increasing attention. However, existing methods still face challenges when dealing with remote sensing image detection tasks, such as insufficient accuracy in small object recognition, an imbalance between recognition precision and detection efficiency, and difficulties in handling multi-scale variations. Based on the above problems, this letter proposes a lightweight and efficient remote sensing image object detector MERS-Net. Firstly, we propose an enhanced feature extraction module SCFE based on sub-channel mixing to improve the feature extraction capability of the model under complex scale transformation. Secondly, we incorporated the WaveletPool module along with the GSConv and VoVGSCSP modules from Slimneck into the model to reduce computational parameters. Finally, we designed a lightweight detection head MRF-Detect based on the parameter sharing mechanism to improve the recognition capability of the model under limited hardware conditions. We verified the effectiveness of MERS-Net on the public datasets DOTA, AI-TOD, and DIOR datasets. Compared with current mainstream algorithms such as YOLOv11, the average accuracy (mAP) is improved by 2.6%, 2.5% and 2.7%, respectively. At the same time, the parameter quantity and GFLOPs are significantly reduced, showing its superior detection performance.