Enhancing remote sensing object detection via selective-perspective-class integration

Xinyuan Wang

doi:10.1016/j.engappai.2025.113416

Enhancing remote sensing object detection via selective-perspective-class integration

2025-12-05

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DOI：10.1016/j.engappai.2025.113416

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Object detection in remote sensing imagery is persistently challenged by extreme scale variations, densely distributed objects, and cluttered backgrounds. Although modern detectors like YOLOv8 have shown promising results, their backbone networks often lack explicit mechanisms to guide multi-scale feature refinement, which constrains their performance on high-resolution aerial imagery. In this work, we propose You Only Look Once-Selective-Perspective-Class Integration (YOLO-SPCI), an attention-enhanced detection framework that introduces a lightweight Selective-Perspective-Class Integration (SPCI) module to improve feature representation. The SPCI module integrates three components: a Selective Stream Gate (SSG) for adaptive regulation of global feature flow, a Perspective Fusion Module (PFM) for context-aware multi-scale integration, and a Class Discrimination Module (CDM) to enhance inter-class separability. We embed two SPCI blocks into the P3 and P5 stages of the YOLOv8 backbone, facilitating effective multi-scale feature refinement while maintaining full compatibility with the original neck and head structures. Experiments on the Northwestern Polytechnical University Very High Resolution-10 dataset (NWPU VHR-10), Dataset for Object Detection in Optical Remote Sensing Images (DIOR), and Remote Sensing Object Detection dataset (RSOD) demonstrate that YOLO-SPCI achieves superior performance compared to state-of-the-art detectors.

Enhancing remote sensing object detection via selective-perspective-class integration

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