# MO R-CNN: Multispectral Oriented R-CNN for Object Detection in Remote Sensing Image Source: [MO R-CNN: Multispectral Oriented R-CNN for Object Detection in Remote Sensing Image]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:40:29.919Z Authors: Leiyu Wang, Biao Jin, Feng Huang, Liqiong Chen, Zhengyong Wang, Xiaohai He, Honggang Chen Published: 2025-09-21T07:38:54.000Z ## Abstract Oriented object detection for multi-spectral imagery faces significant challenges due to differences both within and between modalities. Although existing methods have improved detection accuracy through complex network architectures, their high computational complexity and memory consumption severely restrict their performance. Motivated by the success of large kernel convolutions in remote sensing, we propose MO R-CNN, a lightweight framework for multi-spectral oriented detection featuring heterogeneous feature extraction network (HFEN), single modality supervision (SMS), and condition-based multimodal label fusion (CMLF). HFEN leverages inter-modal differences to adaptively align, merge, and enhance multi-modal features. SMS constrains multi-scale features and enables the model to learn from multiple modalities. CMLF fuses multimodal labels based on specific rules, providing the model with a more robust and consistent supervisory signal. Experiments on the DroneVehicle, VEDAI and OGSOD datasets prove the superiority of our method. The source code is available at:https://github.com/Iwill-github/MORCNN. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()