Global Relation-Aware-Based Oil Detection Method for Water Surface of Catchment Wells in Hydropower Stations
Abstract
:1. Introduction
- Considering the problems of low brightness, high noise, and few extractable feature points in the image sample data caused by the catchment well environment, a sample data enhancement method is proposed. This method enhances the edge details of oil in the sample data through single-scale retinex histogram equalization, and dynamically stretches the overall image to enhance the contrast of oil in the image.
- For the purpose of strengthening the feature extraction of sample data in the detection phase, we propose an improved Yolov5s method. In this method, the backbone network is enhanced by a global relation-aware attention mechanism that highlights the main features of sample data and suppresses minor information at both the spatial and channel levels. This results in improved detection accuracy and a stronger anti-interference capability against the background.
- A deep learning-based method for pixel-level oil detection of catchment wells in hydropower stations is proposed, which has a good economic efficiency compared to other oil detection methods.
2. Proposed Methods
2.1. Yolov5s
2.2. Global Relation-Aware-Based Yolov5s Algorithm
2.3. Single-Scale Retinex Histogram Equalization
3. Experimental Results and Discussion
3.1. Experimental Environments and Parameters
3.2. Experimental Data Sources
3.3. Results and Analysis
4. Conclusions
- (1)
- For the problems of poor sample quality and limited extractable feature points caused by the environment and insufficient light on the water surface of the catchment wells, a sample data preprocessing method is proposed. The sample dataset is processed using the SSR histogram equalization method to obtain images both of high brightness and of better visual effects of the oil pollution on the water’s surface, which benefits the deep learning model’s extraction of the target features of the oil pollution.
- (2)
- Considering the issues of missed and false detections in Yolov5s when detecting oil pollution, a global relationship-aware attention mechanism is embedded in the backbone network of Yolov5s to enhance the feature extraction of oil pollution, thus suppressing the interference of noise on detection and improving the detection accuracy of the algorithm.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Methods | P/% | R/% | APe/% | APt/% | mAP@0.5/% |
---|---|---|---|---|---|
Yolov5s | 87.1 | 85.7 | 87.3 | 79.1 | 83.2 |
Yolov5s + RGA-S | 90.6 | 89.9 | 92.3 | 84.2 | 88.3 |
Yolov5s + RGA-C | 91.9 | 87.1 | 91.8 | 86.1 | 90.0 |
Yolov5s + RGA-SC | 93.6 | 90.3 | 93.9 | 87.6 | 90.8 |
Yolov5s + SSR Histogram | 89.7 | 86.0 | 91.0 | 81.2 | 86.1 |
The proposed algorithm | 93.5 | 89.7 | 94.1 | 89.0 | 91.6 |
A | B | C | D | E | F | |
---|---|---|---|---|---|---|
Image | ||||||
Yolov5s | ||||||
Improved algorithms |
Algorithms | P/% | R/% | FPS/s | mAP@0.5/% | Weight/MB |
---|---|---|---|---|---|
Yolov5s | 87.2 | 84.3 | 190 | 86.7 | 14.8 |
Faster R-CNN | 85.7 | 83.1 | 24 | 79.3 | 547.0 |
FSSD | 91.3 | 88.0 | 82 | 90.4 | 128.8 |
SSD | 88.6 | 77.5 | 90 | 89.1 | 126.7 |
Improved algorithms | 94.0 | 89.9 | 185 | 93.0 | 15.0 |
Category 1 | Category 2 | Category 3 | Category 4 | |
---|---|---|---|---|
Yolov5s | ||||
Faster R-CNN | ||||
FSSD | ||||
SSD | ||||
Improved algorithms |
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Liu, J.; Lin, H.; Liu, Y.; Xiong, L.; Li, C.; Zhou, T.; Ma, M. Global Relation-Aware-Based Oil Detection Method for Water Surface of Catchment Wells in Hydropower Stations. Sustainability 2023, 15, 6966. https://doi.org/10.3390/su15086966
Liu J, Lin H, Liu Y, Xiong L, Li C, Zhou T, Ma M. Global Relation-Aware-Based Oil Detection Method for Water Surface of Catchment Wells in Hydropower Stations. Sustainability. 2023; 15(8):6966. https://doi.org/10.3390/su15086966
Chicago/Turabian StyleLiu, Jiajun, Haokun Lin, Yue Liu, Lei Xiong, Chenjing Li, Tinghu Zhou, and Mike Ma. 2023. "Global Relation-Aware-Based Oil Detection Method for Water Surface of Catchment Wells in Hydropower Stations" Sustainability 15, no. 8: 6966. https://doi.org/10.3390/su15086966