Zhuang Characteristic Culture Detection Based on Improved YOLOV3-SPP Network

Guo, Shaojun; Qiang, Baohua; Xie, Wu; Zhai, Yijie; Chen, Ruidong; Zheng, Hong

doi:10.1007/978-981-16-8430-2_20

Cited by 1 publication

(1 citation statement)

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“…In this section, we compared the performance of our proposed YOLOv5-MR model against several state-of-the-art object detection models, including Ultralytics5.0 version of YOLOv3 [ 20 ], YOLOv3-spp [ 35 ], and YOLOv5 [ 22 ]. The models were trained and validated using the MR-Meter dataset, and the comparison was conducted in terms of Recall, mAP, GFLOPS, and Weights, as presented in Table 4 .…”

Section: Experimental and Results Analysismentioning

confidence: 99%

Automatic Recognition Reading Method of Pointer Meter Based on YOLOv5-MR Model

Zou

Wang

Zhang

et al. 2023

Sensors

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Meter reading is an important part of intelligent inspection, and the current meter reading method based on target detection has problems of low accuracy and large error. In order to improve the accuracy of automatic meter reading, this paper proposes an automatic reading method for pointer-type meters based on the YOLOv5-Meter Reading (YOLOv5-MR) model. Firstly, in order to improve the detection performance of small targets in YOLOv5 framework, a multi-scale target detection layer is added to the YOLOv5 framework, and a set of Anchors is designed based on the lightning rod dial data set; secondly, the loss function and up-sampling method are improved to enhance the model training convergence speed and obtain the optimal up-sampling parameters; Finally, a new external circle fitting method of the dial is proposed, and the dial reading is calculated by the center angle algorithm. The experimental results on the self-built dataset show that the Mean Average Precision (mAP) of the YOLOv5-MR target detection model reaches 79%, which is 3% better than the YOLOv5 model, and outperforms other advanced pointer-type meter reading models.

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Section: Experimental and Results Analysismentioning

confidence: 99%