A real-time detection for miner behavior via DYS-YOLOv8n model

IF 2.9 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Real-Time Image Processing Pub Date : 2024-05-13 DOI:10.1007/s11554-024-01466-0

Fangfang Xin, Xinyu He, Chaoxiu Yao, Shan Li, Biao Ma, Hongguang Pan

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引用次数: 0

Abstract

To address the issues of low real-time performance and poor algorithm accuracy in detecting miner behavior underground, we propose a high-precision real-time detection method named DSY-YOLOv8n based on the characteristics of human body behavior. This method integrates DSConv into the backbone network to enhance multi-scale feature extraction. Additionally, SCConv-C2f replaces C2f modules, reducing redundant calculations and improving model training speed. The optimization strategy of the loss function is employed, and MPDIoU is used to improve the model’s accuracy and speed. The experimental results show: (1) With almost no increase in parameters and calculation amount, the mAP50 of the DSY-YOLOv8n model is 97.4%, which is a 3.2% great improvement over the YOLOv8n model. (2) Compared to Faster-R-CNN, YOLOv5s, and YOLOv7, DYS-YOLOv8n has improved the average accuracy to varying degrees while significantly increasing the detection speed. (3) DYS-YOLOv8n meets the real-time requirements for behavioral detection in mines with a detection speed of 243FPS. In summary, the DYS-YOLOv8n offers a real-time, efficient, and lightweight method for detecting miner behavior in mines, which has high practical value.

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通过 DYS-YOLOv8n 模型实时检测矿工行为

针对井下矿工行为检测实时性低、算法准确性差等问题，我们提出了一种基于人体行为特征的高精度实时检测方法，命名为 DSY-YOLOv8n。该方法将 DSConv 集成到主干网络中，以加强多尺度特征提取。此外，SCConv-C2f 取代了 C2f 模块，减少了冗余计算，提高了模型训练速度。采用损失函数的优化策略，并使用 MPDIoU 来提高模型的精度和速度。实验结果表明：（1）在几乎不增加参数和计算量的情况下，DSY-YOLOv8n 模型的 mAP50 为 97.4%，比 YOLOv8n 模型提高了 3.2%。(2）与 Faster-R-CNN、YOLOv5s 和 YOLOv7 相比，DYS-YOLOv8n 在显著提高检测速度的同时，平均准确率也有不同程度的提高。(3）DYS-YOLOv8n 以 243FPS 的检测速度满足了矿井行为检测的实时性要求。综上所述，DYS-YOLOv8n 提供了一种实时、高效、轻便的矿井矿工行为检测方法，具有很高的实用价值。

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来源期刊

Journal of Real-Time Image Processing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

6.80

自引率

6.70%

发文量

审稿时长

6 months

期刊介绍： Due to rapid advancements in integrated circuit technology, the rich theoretical results that have been developed by the image and video processing research community are now being increasingly applied in practical systems to solve real-world image and video processing problems. Such systems involve constraints placed not only on their size, cost, and power consumption, but also on the timeliness of the image data processed. Examples of such systems are mobile phones, digital still/video/cell-phone cameras, portable media players, personal digital assistants, high-definition television, video surveillance systems, industrial visual inspection systems, medical imaging devices, vision-guided autonomous robots, spectral imaging systems, and many other real-time embedded systems. In these real-time systems, strict timing requirements demand that results are available within a certain interval of time as imposed by the application. It is often the case that an image processing algorithm is developed and proven theoretically sound, presumably with a specific application in mind, but its practical applications and the detailed steps, methodology, and trade-off analysis required to achieve its real-time performance are not fully explored, leaving these critical and usually non-trivial issues for those wishing to employ the algorithm in a real-time system. The Journal of Real-Time Image Processing is intended to bridge the gap between the theory and practice of image processing, serving the greater community of researchers, practicing engineers, and industrial professionals who deal with designing, implementing or utilizing image processing systems which must satisfy real-time design constraints.