Video detection of foreign objects on the surface of belt conveyor underground coal mine based on improved SSD

Wang, Yuanbin; Wang, Yujing; Dang, L. Minh

doi:10.1007/s12652-020-02495-w

Cited by 26 publications

(17 citation statements)

References 11 publications

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“…Specifically, in terms of the richness of foreign object types, as compared to [ 14 , 15 , 18 , 19 ], the approach presented in this study exhibits superior performance in terms of both detection speed and accuracy by providing a more detailed classification of foreign objects transported (including six common types). In terms of the comparison of network model parameters and computational complexity, as compared to [ 16 , 17 ], the improved model in this study, although not achieving the highest accuracy, exhibits outstanding parameter efficiency (4.1 M) and FPS (92.5), which are more favorable for edge devices with limited computational capabilities. It is worth noting that the improved model in this study exhibits lower detection accuracy compared to the R-CNN network.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

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A Faster and Lighter Detection Method for Foreign Objects in Coal Mine Belt Conveyors

Luo

Kou

Han

et al. 2023

Sensors

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Coal flow in belt conveyors is often mixed with foreign objects, such as anchor rods, angle irons, wooden bars, gangue, and large coal chunks, leading to belt tearing, blockages at transfer points, or even belt breakage. Fast and effective detection of these foreign objects is vital to ensure belt conveyors’ safe and smooth operation. This paper proposes an improved YOLOv5-based method for rapid and low-parameter detection and recognition of non-coal foreign objects. Firstly, a new dataset containing foreign objects on conveyor belts is established for training and testing. Considering the high-speed operation of belt conveyors and the increased demands for inspection robot data collection frequency and real-time algorithm processing, this study employs a dark channel dehazing method to preprocess the raw data collected by the inspection robot in harsh mining environments, thus enhancing image clarity. Subsequently, improvements are made to the backbone and neck of YOLOv5 to achieve a deep lightweight object detection network that ensures detection speed and accuracy. The experimental results demonstrate that the improved model achieves a detection accuracy of 94.9% on the proposed foreign object dataset. Compared to YOLOv5s, the model parameters, inference time, and computational load are reduced by 43.1%, 54.1%, and 43.6%, respectively, while the detection accuracy is improved by 2.5%. These findings are significant for enhancing the detection speed of foreign object recognition and facilitating its application in edge computing devices, thus ensuring belt conveyors’ safe and efficient operation.

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Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

“…Yuanbin Wang et al [ 16 ] proposed an improved foreign object recognition method based on Single Shot MultiBox Detector (SSD) and modified the model’s loss function. Compared to YOLOv3, their approach achieved a higher detection accuracy of 90.2%.…”

Section: Introductionmentioning

confidence: 99%

A Faster and Lighter Detection Method for Foreign Objects in Coal Mine Belt Conveyors

Luo

Kou

Han

et al. 2023

Sensors

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“…The model is excessively huge, the detection is heavily GPU-dependent, and the real-time performance is high. Wang et al [12] proposed an SSD-based video method for detecting foreign bodies on the surface of a belt conveyor. The method used was the deep separable convolution method to reduce the number of parameters in the SSD algorithm and improve the speed.…”

Section: Related Workmentioning

confidence: 99%

Foreign Object Detection Method of Conveyor Belt Based on Improved Nanodet

2023

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“…In the process of coal production, coal is frequently mixed with gangue, woods, anchor rods, woven bags, iron, and other foreign objects, which seriously affects the safe, efficient, and green production of coal mines [ 2 , 3 ], and it is urgent to choose an automated and intelligent foreign object detection and separation method [ 4 ]. With the rapid development of coal mine intelligence, foreign object detection methods based on deep learning have received wide attention from scholars [ 5 ]. However, deep learning is a data-driven method [ 6 ], and the training process usually requires the support of large datasets to prevent model overfitting.…”

Section: Introductionmentioning

confidence: 99%

High Quality Coal Foreign Object Image Generation Method Based on StyleGAN-DSAD

Cao

Wei

Wang

et al. 2022

Sensors

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Research on coal foreign object detection based on deep learning is of great significance to safe, efficient, and green production of coal mines. However, the foreign object image dataset is scarce due to collection conditions, which brings an enormous challenge to coal foreign object detection. To achieve augmentation of foreign object datasets, a high-quality coal foreign object image generation method based on improved StyleGAN is proposed. Firstly, the dual self-attention module is introduced into the generator to strengthen the long-distance dependence of features between spatial and channel, refine the details of the generated images, accurately distinguish the front background information, and improve the quality of the generated images. Secondly, the depthwise separable convolution is introduced into the discriminator to solve the problem of low efficiency caused by the large number of parameters of multi-stage convolutional networks, to realize the lightweight model, and to accelerate the training speed. Experimental results show that the improved model has significant advantages over several classical GANS and original StyleGAN in terms of quality and diversity of the generated images, with an average improvement of 2.52 in IS and a decrease of 5.80 in FID for each category. As for the model complexity, the parameters and training time of the improved model are reduced to 44.6% and 58.8% of the original model without affecting the generated images quality. Finally, the results of applying different data augmentation methods to the foreign object detection task show that our image generation method is more effective than the traditional methods, and that, under the optimal conditions, it improves APbox by 5.8% and APmask by 4.5%.

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Video detection of foreign objects on the surface of belt conveyor underground coal mine based on improved SSD

Cited by 26 publications

References 11 publications

A Faster and Lighter Detection Method for Foreign Objects in Coal Mine Belt Conveyors

A Faster and Lighter Detection Method for Foreign Objects in Coal Mine Belt Conveyors

Foreign Object Detection Method of Conveyor Belt Based on Improved Nanodet

High Quality Coal Foreign Object Image Generation Method Based on StyleGAN-DSAD

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