Fine-grained object detection method using attention mechanism and its application in coal–gangue detection

Lv, Ziqi; Wang, Weidong; Xu, Zhiqiang; Zhang, Kanghui; Fan, Yuhan; Song, Yang

doi:10.1016/j.asoc.2021.107891

Cited by 34 publications

(6 citation statements)

References 21 publications

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“…It is crucial for the identification of tea diseases as disease features are often embodied in local areas of the image. By emphasizing these key local features, the characteristics of tea diseases can be captured more accurately, and more important local features can be given greater weight, achieving more accurate and reliable processing of fine-grained identification and disease identification [24]. Combining lightweight applicability, computational efficiency, and local detail attention considerations, the introduction of spatial attention mechanisms enables the model to improve its sensitivity to key features.…”

Section: B Spatial-attention (Sa) Mechanismmentioning

confidence: 99%

An Improved MobileNet Model Integrated Spatial and Channel Attention Mechanisms for Tea Disease

Zhang,

Sun,

Yang

2024

ijacsa

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Aiming at addressing the challenges of large model parameters, high computational cost, and low accuracy of the traditional tea disease identification model, an improved MobileNet model integrated spatial and channel attention mechanisms (MobileNet-SCA) was proposed for tea disease identification. Firstly, the tea disease identification dataset was augmented through random clipping, rotation transformation, and perspective transformation to simulate diverse image acquisition perspectives and mitigate overfitting effects. Secondly, based on the convolutional neural network (CNN) framework, the Channel Attention (CA) mechanism and Spatial Attention (SA) mechanism were introduced to carry out global average pooling and group normalization operations on input feature maps respectively, and adjust the channel weights using the learned parameters. Then the h-swish activation function was utilized to scale, and the two kinds of attention mechanisms were spliced and mixed to improve the channel and spatial information. In addition, the MobileNetV3 network's structure underwent optimization by adjusting the number of input channels, the size of the convolution kernel, and the number of channels in the residual block. The experimental results showed that the identification accuracy of MobileNet-SCA for tea diseases was 5.39% higher than the original model. This method can balance the identification accuracy and identification time well, and it meets the requirements for accurate and rapid identification of tea diseases.

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Section: B Spatial-attention (Sa) Mechanismmentioning

confidence: 99%

An Improved MobileNet Model Integrated Spatial and Channel Attention Mechanisms for Tea Disease

Zhang,

Sun,

Yang

2024

ijacsa

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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.…”

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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“…Ziqi Lv et al [ 11 ] used the convolutional neural network (CNN) for online detection of coal and gangue, with a detection accuracy of 91.375. Ziqi Lv et al [ 12 ] proposed a single-shot fine-grained object detector using the attention mechanism and applied it to coal–gangue images in a coal preparation plant with APiou = 0.5. Pu et al [ 13 ] used convolutional neural networks and transfer learning to realize the image recognition of coal gangue.…”

Section: Introductionmentioning

confidence: 99%

Detection of Coal and Gangue Based on Improved YOLOv8

Zeng,

Zhou,

Wan

et al. 2024

Sensors

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To address the lightweight and real-time issues of coal sorting detection, an intelligent detection method for coal and gangue, Our-v8, was proposed based on improved YOLOv8. Images of coal and gangue with different densities under two diverse lighting environments were collected. Then the Laplacian image enhancement algorithm was proposed to improve the training data quality, sharpening contours and boosting feature extraction; the CBAM attention mechanism was introduced to prioritize crucial features, enhancing more accurate feature extraction ability; and the EIOU loss function was added to refine box regression, further improving detection accuracy. The experimental results showed that Our-v8 for detecting coal and gangue in a halogen lamp lighting environment achieved excellent performance with a mean average precision (mAP) of 99.5%, was lightweight with FLOPs of 29.7, Param of 12.8, and a size of only 22.1 MB. Additionally, Our-v8 can provide accurate location information for coal and gangue, making it ideal for real-time coal sorting applications.

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Fine-grained object detection method using attention mechanism and its application in coal–gangue detection

Cited by 34 publications

References 21 publications

An Improved MobileNet Model Integrated Spatial and Channel Attention Mechanisms for Tea Disease

An Improved MobileNet Model Integrated Spatial and Channel Attention Mechanisms for Tea Disease

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

Detection of Coal and Gangue Based on Improved YOLOv8

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