A joint algorithm of multi-target detection and tracking for underground miners
The existing multi-target tracking algorithms for underground miners has the problems of slow detection speed and low recognition precision. In order to solve the above problems, a joint algorithm of multi-target detection and tracking algorithm based on the improved YOLOv5s model and the improved D...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Industry and Mine Automation
2022-10-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2022060040 |
| _version_ | 1797869024968704000 |
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| author | ZHOU Mengran LI Xuesong ZHU Ziwei HUANG Kaiwen |
| author_facet | ZHOU Mengran LI Xuesong ZHU Ziwei HUANG Kaiwen |
| author_sort | ZHOU Mengran |
| collection | DOAJ |
| description | The existing multi-target tracking algorithms for underground miners has the problems of slow detection speed and low recognition precision. In order to solve the above problems, a joint algorithm of multi-target detection and tracking algorithm based on the improved YOLOv5s model and the improved Deep SORT algorithm is proposed. In the part of multi-target detection, the YOLOv5s-GAD model is obtained by improving YOLOv5s model. The GhostConv module and the depthwise separable convolution (DWConv) module are introduced to replace the BottleneckCSP module in the YOLOv5s model backbone network and path aggregation network respectively. Therefore, the feature extraction speed is improved. Considering the characteristics of dark underground light and many noisy images, the efficient channel attention neural network (ECA-Net) module is introduced into the minimum feature map to improve the model's overall precision. In the part of multi-target tracking, the omni-scale network (OSNet) is used to replace the shallow residual network in Deep SORT to carry out omni-directional feature learning. Therefore, pedestrian re-identification and target tracking precision are improved. The experimental result shows that on the custom dataset Miner21, the YOLOv5s-GAD model average preciscom (when the intersection of union ratio is 0.5) reaches 97.8%, and the frame rate reaches 140.2 frames/s. The multi-target detection effect is better than the commonly used Faster RCNN, YOLOv3 and YOLOv5s models. On the public miners dataset MOT17, the speed and accuracy of the multi-target detection and tracking joint algorithm are better than those of IOU17, Deep SORT and other common multi-target tracking algorithms. The proposed model has the least number of personnel identity conversions and the best miner re-recognition effect. The joint algorithm of multi-target detection and tracking for underground miners can detect and track underground miners in time, and the multi-target tracking effect is good. |
| first_indexed | 2024-04-10T00:06:09Z |
| format | Article |
| id | doaj.art-ee06611923354027975bd17d6013c18d |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-04-10T00:06:09Z |
| publishDate | 2022-10-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-ee06611923354027975bd17d6013c18d2023-03-17T01:01:13ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2022-10-014810404710.13272/j.issn.1671-251x.2022060040A joint algorithm of multi-target detection and tracking for underground minersZHOU MengranLI XuesongZHU ZiweiHUANG KaiwenThe existing multi-target tracking algorithms for underground miners has the problems of slow detection speed and low recognition precision. In order to solve the above problems, a joint algorithm of multi-target detection and tracking algorithm based on the improved YOLOv5s model and the improved Deep SORT algorithm is proposed. In the part of multi-target detection, the YOLOv5s-GAD model is obtained by improving YOLOv5s model. The GhostConv module and the depthwise separable convolution (DWConv) module are introduced to replace the BottleneckCSP module in the YOLOv5s model backbone network and path aggregation network respectively. Therefore, the feature extraction speed is improved. Considering the characteristics of dark underground light and many noisy images, the efficient channel attention neural network (ECA-Net) module is introduced into the minimum feature map to improve the model's overall precision. In the part of multi-target tracking, the omni-scale network (OSNet) is used to replace the shallow residual network in Deep SORT to carry out omni-directional feature learning. Therefore, pedestrian re-identification and target tracking precision are improved. The experimental result shows that on the custom dataset Miner21, the YOLOv5s-GAD model average preciscom (when the intersection of union ratio is 0.5) reaches 97.8%, and the frame rate reaches 140.2 frames/s. The multi-target detection effect is better than the commonly used Faster RCNN, YOLOv3 and YOLOv5s models. On the public miners dataset MOT17, the speed and accuracy of the multi-target detection and tracking joint algorithm are better than those of IOU17, Deep SORT and other common multi-target tracking algorithms. The proposed model has the least number of personnel identity conversions and the best miner re-recognition effect. The joint algorithm of multi-target detection and tracking for underground miners can detect and track underground miners in time, and the multi-target tracking effect is good.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2022060040coal mine safetymulti-target detection and trackingminer re-recognitionyolov5syolov5s-gaddeep sortomni-scale network |
| spellingShingle | ZHOU Mengran LI Xuesong ZHU Ziwei HUANG Kaiwen A joint algorithm of multi-target detection and tracking for underground miners Gong-kuang zidonghua coal mine safety multi-target detection and tracking miner re-recognition yolov5s yolov5s-gad deep sort omni-scale network |
| title | A joint algorithm of multi-target detection and tracking for underground miners |
| title_full | A joint algorithm of multi-target detection and tracking for underground miners |
| title_fullStr | A joint algorithm of multi-target detection and tracking for underground miners |
| title_full_unstemmed | A joint algorithm of multi-target detection and tracking for underground miners |
| title_short | A joint algorithm of multi-target detection and tracking for underground miners |
| title_sort | joint algorithm of multi target detection and tracking for underground miners |
| topic | coal mine safety multi-target detection and tracking miner re-recognition yolov5s yolov5s-gad deep sort omni-scale network |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2022060040 |
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