EYOLOv3: An Efficient Real-Time Detection Model for Floating Object on River

At present, the surveillance of river floating in China is labor-intensive, time-consuming, and may miss something, so a fast and accurate automatic detection method is necessary. The two-stage convolutional neural network models appear to have high detection accuracy, but it is hard to reach real-t...

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Bibliographic Details
Published in:Applied sciences 2023-02, Vol.13 (4), p.2303
Main Authors: Zhang, Lili, Xie, Zhiqiang, Xu, Mengqi, Zhang, Yi, Wang, Gaoxu
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Clustering
Datasets
Deep learning
Design
Drinking water
Environmental impact
Methods
multi-scale feature
Neural networks
Real time
real-time object detection
Rivers
Streams
Video data
video streaming
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Summary:At present, the surveillance of river floating in China is labor-intensive, time-consuming, and may miss something, so a fast and accurate automatic detection method is necessary. The two-stage convolutional neural network models appear to have high detection accuracy, but it is hard to reach real-time detection, while on the other hand, the one-stage models are less time-consuming but have lower accuracy. In response to the above problems, we propose a one-stage object detection model EYOLOv3 to achieve real-time and high accuracy detection of floating objects in video streams. Firstly, we design a multi-scale feature extraction and fusion module to improve the feature extraction capability of the network. Secondly, a better clustering algorithm is used to analyze the size characteristics of floating objects to design the anchor box, enabling the network to detect objects more effectively. Then a focus loss function is proposed to make the network effectively overcome the sample imbalance problem, and finally, an improved NMS algorithm is proposed to solve the object suppressed problem. Experiments show that the proposed model is efficient in detection of river floating objects, and has better performance than the classical object detection method and the latest method, realizing real-time floating detection in video streams.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13042303