.png){kind=icon}
返回
Hybrid Compression Optimization Based Rapid Detection Method for Non-Coal Conveying Foreign Objects
.png){kind=icon}
OA
AI
DOI：10.3390/mi13122085.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The existence of conveyor foreign objects poses a serious threat to the service life of conveyor belts, which will cause abnormal damage or even tearing, so fast and effective detection of conveyor foreign objects is of great significance to ensure the safe and efficient operation of belt conveyors. Considering the need for the foreign object detection algorithm to operate in edge computing devices, this paper proposes a hybrid compression method that integrates network sparse, structured pruning, and knowledge distillation to compress the network parameters and calculations. Combined with a Yolov5 network for practice, three structured pruning strategies are specifically proposed, all of which are proven to have achieved a good compression effect. The experiment results show that under the pruning rate of 0.9, the proposed three pruning strategies can achieve more than 95% compression for network parameters, more than 90% compression for the computation, and more than 90% compression for the size of the network model, and the optimized network is able to accelerate inference on both Central Processing Unit (CPU) and Graphic Processing Unit (GPU) hardware platforms, with a maximum speedup of 70.3% on the GPU platform and 157.5% on the CPU platform, providing an excellent real-time performance but also causing a large accuracy loss. In contrast, the proposed method balances better real-time performance and detection accuracy (>88.2%) when the pruning rate is at 0.6 similar to 0.9. Further, to avoid the influence of motion blur, a method of introducing prior knowledge is proposed to improve the resistance of the network, thus strongly ensuring the detection effect. All the technical solutions proposed are of great significance in promoting the intelligent development of coal mine equipment, ensuring the safe and efficient operation of belt conveyors, and promoting sustainable development.
Keyword:
foreign object detection
belt conveyor
deep learning
computer vision
lightweight CNNs
network compression
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
1.4W
被引数:
2.9W
机构
暂无机构信息
引用论文
Mechanism Analysis of Roadway Rockbursts Induced by Dynamic Mining Loading and Its Application.png)
EnergiesOAAI
EnergiesIF0
OAAI分享收藏Separation Between Coal and Gangue Based on Infrared Radiation and Visual Extraction of the YCbCr Color SpaceIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容