YOLO-SPCI: Enhancing Remote Sensing Object Detection via Selective-Perspective-Class Integration

TL;DR

YOLO-SPCI introduces a novel attention-based framework with a specialized module to improve multi-scale feature refinement for remote sensing object detection, achieving superior results on aerial imagery datasets.

Contribution

The paper presents the SPCI module, combining selective gating, perspective fusion, and class discrimination, integrated into YOLOv8 to enhance feature representation for remote sensing images.

Findings

Outperforms state-of-the-art detectors on NWPU VHR-10 dataset

Improves multi-scale feature refinement in YOLOv8

Demonstrates effectiveness of SPCI modules in remote sensing detection

Abstract

Object detection in remote sensing imagery remains a challenging task due to extreme scale variation, dense object distributions, and cluttered backgrounds. While recent detectors such as YOLOv8 have shown promising results, their backbone architectures lack explicit mechanisms to guide multi-scale feature refinement, limiting performance on high-resolution aerial data. In this work, we propose 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, enabling effective refinement while preserving compatibility with the original neck and head. Experiments on the NWPU VHR-10 dataset demonstrate that YOLO-SPCI achieves superior performance compared to state-of-the-art detectors.