MCI-Net: Attention Enhanced Convolutional Network Based on Remote Sensing Images

Remote sensing image object detection is currently a research hotspot, but its performance still faces fundamental challenges: existing methods have not fully exploited the unique prior knowledge of remote sensing scenes, such as the structure and distribution patterns of ground objects. At the same time, multi-scale objects and high-resolution data in images also bring computational burdens. To this end, this article selects YOLOv12-N, which achieves an excellent balance between detection performance and computational efficiency, as the baseline model and proposes a new object detector for RSI — MCI-Net (Multicore Convolutional IoU Network), which includes multiple components such as MAF (Multicore Attention Framework), CAS (Convolutional Attention Synthesis) and the Shape_IoU. MAF enhances feature representation and improves detail capture ability by introducing multi-scale and large kernel attention. CAS optimizes feature processing, reduces computational and memory requirements, and improves model efficiency. Shape_IoU loss function not only considers the overlapping area between the predicted box and the true box but also pays extra attention to the shape and scale characteristics of the bounding box itself, thereby optimizing detection performance. MCI-Net performs better than the baseline model on the SIMD, DIOR, VHR-10 and DOTA datasets, with improvements of 6.2%, 2.2%, 3.5%, 2.9% on the SIMD dataset, 1.7%, 0.6%, 1.3%, 1.3% on the DIOR dataset, and 0.3%, 0.1%, 0.1%, and 0.1% on the VHR-10 dataset, and 1.9%, 2.7%, 2.2%, 2.7% on the DOTA dateset.