Intelligent Identification Method of Shearer Drums Based on Improved YOLOv5s with Dark Channel-Guided Filtering Defogging

Mao, Qinghua; Wang, Menghan; Hu, Xin; Xue, Xusheng; Zhai, Jingsheng

doi:10.3390/en16104190

Cited by 5 publications

(2 citation statements)

References 17 publications

(20 reference statements)

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“…The SPPF module uses three 5 × 5 maximum pooling layers to effectively solve the problems of incomplete image cropping and shape distortion as well as to obtain more feature information by fusing more features of different resolutions. Compared with the SPP module, the SPPF module reduces the amount of computation while ensuring similar accuracy [27,28].…”

Section: Sheep Face Detection Modulementioning

confidence: 99%

LSR-YOLO: A High-Precision, Lightweight Model for Sheep Face Recognition on the Mobile End

Zhang

Xuan

Jing

et al. 2023

Animals

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The accurate identification of sheep is crucial for breeding, behavioral research, food quality tracking, and disease prevention on modern farms. As a result of the time-consuming, expensive, and unreliable problems of traditional sheep-identification methods, relevant studies have built sheep face recognition models to recognize sheep through facial images. However, the existing sheep face recognition models face problems such as high computational costs, large model sizes, and weak practicality. In response to the above issues, this study proposes a lightweight sheep face recognition model named LSR-YOLO. Specifically, the ShuffleNetv2 module and Ghost module were used to replace the feature extraction module in the backbone and neck of YOLOv5s to reduce floating-point operations per second (FLOPs) and parameters. In addition, the coordinated attention (CA) module was introduced into the backbone to suppress non-critical information and improve the feature extraction ability of the recognition model. We collected facial images of 63 small-tailed Han sheep to construct a sheep face dataset and further evaluate the proposed method. Compared to YOLOv5s, the FLOPs and parameters of LSR-YOLO decreased by 25.5% and 33.4%, respectively. LSR-YOLO achieved the best performance on the sheep face dataset, and the mAP@0.5 reached 97.8% when the model size was only 9.5 MB. The experimental results show that LSR-YOLO has significant advantages in recognition accuracy and model size. Finally, we integrated LSR-YOLO into mobile devices and further developed a recognition system to achieve real-time recognition. The results show that LSR-YOLO is an effective method for identifying sheep. The method has high recognition accuracy and fast recognition speed, which gives it a high application value in mobile recognition and welfare breeding.

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Section: Sheep Face Detection Modulementioning

confidence: 99%

LSR-YOLO: A High-Precision, Lightweight Model for Sheep Face Recognition on the Mobile End

Zhang

Xuan

Jing

et al. 2023

Animals

View full text Add to dashboard Cite

show abstract

“…Yolo uses a convolutional network to extract features, and then uses a fully connected layer to obtain predicted values. The network structure refers to the GooLeNet model and contains 24 convolutional layers and 2 fully connected layers [3]. The output vector of YOLO includes not only the category of the target.…”

Section: Introductionmentioning

confidence: 99%

Research on Improved Method based on YOLOV5s Target Detection Model

Dong,

Li,

Zhou

2023

FCIS

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Aiming at the problem of low detection accuracy of small targets, an object detection method based on average pooling improved YOLOV5s model is proposed. The algorithm introduces the Squeeze Excitation attention module and the Efficient Intersection Over Union loss function to comprehensively improve the detection calculation efficiency and accurate deployment ability. With the development of deep learning technology, which is of great significance to improve the detection accuracy and detection rate. YOLO greatly improves detection performance, three times faster than retinanet and 2 times faster than faster-rcnn. YOLO has strong generalization ability, can be applied to different application scenarios, and is also easy to deploy. The steel surface defect public dataset was selected for verification. The results show that the improved YOLOV5s model is better than the original YOLOV5s model, the test average accuracy mAP can reach 81.8%, the average accuracy mAP of the model is increased by 7.4%, and the overall performance of the improved model is better than other conventional models.

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A Hybrid Strategy Enhanced Sub-Sampled Recursive Second Order Algorithm for Chemical Process Intelligent Identification

Wang

2023

Int J Comput Intell Syst

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The accurate dynamic model of the chemical process is an important condition for the successful implementation of advanced control in the plant. In this paper, an efficient second order algorithm for long short-term memory (LSTM) network training is proposed for chemical process intelligent identification. A novel Hessian inverse recursion method is adopted to achieve fast convergence and avoid the high-cost operation of the classic second order optimization method. Besides, more information is back propagated since the proposed method retains the real curvature information of the neural network. Considering the large amount of chemical process data, a sub-sampled recursive second order-stochastic gradient descent (SRSO-SGD) algorithm which uses sub-sampling method and hybrid strategy is proposed. The identification experiment on a delayed coker fractionator shows that the proposed sub-sampled neural network second order training algorithm has better performance than other learning algorithms in terms of model identification accuracy and convergence speed. By adopting a hybrid strategy that performing Hessian inverse estimation every 3 training epochs, the expensive Hessian inverse calculation cost in the identification process is further reduced while low training and testing errors are maintained.

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Intelligent Identification Method of Shearer Drums Based on Improved YOLOv5s with Dark Channel-Guided Filtering Defogging

Cited by 5 publications

References 17 publications

LSR-YOLO: A High-Precision, Lightweight Model for Sheep Face Recognition on the Mobile End

LSR-YOLO: A High-Precision, Lightweight Model for Sheep Face Recognition on the Mobile End

Research on Improved Method based on YOLOV5s Target Detection Model

A Hybrid Strategy Enhanced Sub-Sampled Recursive Second Order Algorithm for Chemical Process Intelligent Identification

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