# UAV-Based Oil Leakage Spot Detection Under Complex Illumination via a Collaborative Low-Light Enhancement and Detection Framework

## TL;DR

This paper introduces a framework for detecting oil leaks from UAV images under challenging lighting conditions, improving detection accuracy significantly.

## Contribution

A collaborative enhancement and detection framework is proposed to preserve oil leakage details while suppressing background interference under complex illumination.

## Key findings

- The proposed framework achieves 94.25% precision and 87.54% mAP on UAV oilfield datasets.
- The method outperforms existing approaches in detecting oil leakage spots under low light and mixed shadows.
- An improved Retinex-based enhancement network and AC-FPN module effectively preserve details and suppress background interference.

## Abstract

Accurate detection of oil leakage spots is essential for oilfield safety and environmental protection. However, UAV-based inspection in onshore oilfields often suffers from complex illumination conditions, such as low light, backlighting, and mixed shadows, which simultaneously degrade image visibility and obscure leakage-sensitive features, thereby causing missed detection of minute and weak-texture oil leakage targets. Unlike generic low-light enhancement or object detection tasks, the core challenge of onshore UAV oil leakage inspection lies in preserving leakage-oriented fine cues during enhancement while improving the detector’s ability to distinguish leakage targets from highly confusing oilfield backgrounds. To address this task-specific challenge, we propose a collaborative low-light enhancement and detection framework that jointly optimizes leakage-detail-preserving enhancement and multi-scale interference-suppressed detection. Specifically, an improved Retinex-based enhancement network is designed by integrating multi-scale feature aggregation, NAFNet-based denoising, and a CBAM attention mechanism to enhance brightness while preserving leakage details. The enhanced images are then fed into an improved YOLOv11 detector, where an AC-FPN module is adopted to strengthen multi-scale feature fusion and suppress background interference. Experiments on UAV oilfield datasets demonstrate that the proposed method achieves a precision of 94.25% and a mean average precision (mAP) of 87.54%, outperforming existing approaches. The proposed framework provides an effective and robust solution for oil leakage spot detection under complex illumination.