Detection of coal and gangue based on improved YOLOv5.1 which embedded scSE module

Yan, Pengcheng; Sun, Quansheng; Yin, Nini; Hua, Lili; Shang, Songhang; Zhang, Chaoyin

doi:10.1016/j.measurement.2021.110530

Cited by 72 publications

(30 citation statements)

References 23 publications

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“…The left part of Figure 12 shows the graphs of the metrics curves as training progresses. It is proved the detection accuracy of the YOLOv5_Ours model [43]. After evaluation, the YOLOv5_Ours model had a validation precision score of 90.7%, recall score of 87.4%, as well as F1-score of 88.8%, and mAP score is 95.5%.…”

Section: Resultsmentioning

confidence: 77%

Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

2022

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With the recent development of drone technology, object detection technology is emerging, and these technologies can also be applied to illegal immigrants, industrial and natural disasters, and missing people and objects. In this paper, we would like to explore ways to increase object detection performance in these situations. Photography was conducted in an environment where it was confusing to detect an object. The experimental data were based on photographs that created various environmental conditions, such as changes in the altitude of the drone, when there was no light, and taking pictures in various conditions. All the data used in the experiment were taken with F11 4K PRO drone and VisDrone dataset. In this study, we propose an improved performance of the original YOLOv5 model. We applied the obtained data to each model: the original YOLOv5 model and the improved YOLOv5_Ours model, to calculate the key indicators. The main indicators are precision, recall, F-1 score, and mAP (0.5), and the YOLOv5_Ours values of mAP (0.5) and function loss were improved by comparing it with the original YOLOv5 model. Finally, the conclusion was drawn based on the data comparing the original YOLOv5 model and the improved YOLOv5_Ours model. As a result of the analysis, we were able to arrive at a conclusion on the best model of object detection under various conditions.

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Section: Resultsmentioning

confidence: 77%

Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

2022

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“…Considering ubiquitous fine-grained features in industrial object images, Lv et al [ 22 ] proposed a single-shot fine-grained object detector and applied it to coal gangue images in coal preparation plants. Yan [ 23 ] used the YOLOv5 algorithm to analyze spectral images of coal gangue. Liu et al [ 24 ] proposed an improved YOLOv4 algorithm that uses a Laplacian operator and Gaussian filter to reduce mine dust and impact, and has good anti-interference ability.…”

Section: Literature Reviewmentioning

confidence: 99%

A Big Coal Block Alarm Detection Method for Scraper Conveyor Based on YOLO-BS

Wang

Guo

Zhao

et al. 2022

Sensors

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With the aim of solving the problem of coal congestion caused by big coal blocks in underground mine scraper conveyors, in this paper we proposed the use of a YOLO-BS (YOLO-Big Size) algorithm to detect the abnormal phenomenon of coal blocks on scraper conveyors. Given the scale of the big coal block targets, the YOLO-BS algorithm replaces the last layer of the YOLOv4 algorithm feature extraction backbone network with the transform module. The YOLO-BS algorithm also deletes the redundant branch which detects small targets in the PAnet module, which reduces the overall number of parameters in the YOLO-BS algorithm. As the up-sampling and down-sampling operations in the PAnet module of the YOLO algorithm can easily cause feature loss, YOLO-BS improves the problem of feature loss and enhances the convergence performance of the model by adding the SimAM space and channel attention mechanism. In addition, to solve the problem of sample imbalance in big coal block data, in this paper, it was shown that the YOLO-BS algorithm selects focal loss as the loss function. In view of the problem that the same lump coal in different locations on the scraper conveyor led to different congestion rates, we conducted research and proposed a formula to calculate the congestion rate. Finally, we collected 12,000 image datasets of coal blocks on the underground scraper conveyor in Daliuta Coal Mine, China, and verified the performance of the method proposed in this paper. The results show that the processing speed of the proposed method can reach 80 fps, and the correct alarm rate can reach 93%. This method meets the real-time and accuracy requirements for the detection of abnormal phenomena in scraper conveyors.

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“…Previously, attention mechanisms have been tried in other fields combined with YOLOV5. For example, Yan et al [30] combined the SE module with YOLOV5 to improve the accuracy of coalgangue classification. Qi et al [31] achieved high-accuracy recognition of tomato virus disease and improved detection speed based on a YOLOV5 and SE module model.…”

Section: E Improved Yolov5_plusmentioning

confidence: 99%

Detection and Counting Method of Pigs Based on YOLOV5_Plus: A Combination of YOLOV5 and Attention Mechanism

Zishun

2022

Mathematical Problems in Engineering

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Information-based pig detection and counting is the trend in smart animal husbandry development. Cameras can efficiently collect farm information and combine it with artificial intelligence technology to assist breeders in real-time monitoring and analysis of farming. In order to improve the speed and accuracy of pig detection and counting, an advanced improved YOLO_v5 method for pig detection and counting based on the attention mechanism is proposed. The model is named as YOLOV5_Plus. This article utilizes a series of data augmentation methods, including translation, color augmentation, rescaling, and mosaic. The proposed model performs feature extraction on the original image with a backbone network, detects pigs of different sizes with three detection heads, and counts the detected anchor frames. Different versions of YOLOV5 are compared, and YOLOV5x is selected as the baseline model for the best performance. Attention modules are smartly combined with the model so that the model can better handle overlapping and misidentification. YOLOV5_Plus can achieve an accuracy of 0.989, a recall of 0.996, mAP@.50 of 0.994, and mAP@.50:.95 of 0.796, which outperforms all competing models. The inference time per image during detection is only 24.1 ms. YOLOV5_Plus model achieves real-time pig number and location detection, which is meaningful for promoting smart animal husbandry and saving labor costs in farming enterprises.

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Detection of coal and gangue based on improved YOLOv5.1 which embedded scSE module

Cited by 72 publications

References 23 publications

Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

A Big Coal Block Alarm Detection Method for Scraper Conveyor Based on YOLO-BS

Detection and Counting Method of Pigs Based on YOLOV5_Plus: A Combination of YOLOV5 and Attention Mechanism

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