Waste-YOLO: towards high accuracy real-time abnormal waste detection in waste-to-energy power plant for production safety

Automotive paint defect detection plays a crucial role in the automotive production process. Current research on visual defect detection methods is mainly based on supervised learning, which requires a large number of labeled image samples for model training. The labeling work is not only time consuming but also expensive, seriously hindering the testing and application of these models in practice. To address this issue, this study proposes a new method for automotive paint defect detection based on a semi-supervised training strategy. First, a semi-supervised automotive paint defect detection framework, which can use labeled and unlabeled samples to reduce the cost of data labeling effectively, is presented. Then, a spatial pyramid pooling fast external attention module that introduces an external attention mechanism is proposed to improve the traditional YOLOv7 network structure, called YOLOv7-EA, to obtain good detection performance. This network acts as a detector to generate high-quality pseudo labels for the unlabeled samples, providing additional data to train the model; meanwhile, it performs the final detection task. Lastly, a Wise-intersection over union loss function that considers the quality of the anchor box is introduced to reduce the interference of low-quality samples and improve the convergence speed and detection accuracy of the model. Using this method, we can accomplish the task of automotive paint defect detection with a small number of labeled image samples. Experimental results on the automotive paint defect dataset show that mean average precision (mAp)@.5, mAp@.75, and mAp@.5:.95 are superior to other methods under the condition of 10% and 15% labeled data, achieving good defect detection performance.

Section: Feature Extraction Module Sppfeamentioning

confidence: 99%

Semi-supervised method for visual detection of automotive paint defects

Jiang,

Chen,

et al. 2024

“…The Yolo series models, known for their ease of training and deployment and their ability to strike a balance between accuracy and speed, have gained widespread development [12][13][14]. Numerous scholars have devoted themselves to optimizing parts of Yolo series models to improve performance.…”

Section: Introductionmentioning

confidence: 99%

High-accuracy and lightweight weld surface defect detector based on graph convolution decoupling head

Wang,

Chen,

Lin

et al. 2024

The essence of the difficulties for weld surface detection is that there is a lot of interference information during detection. This study aims to enhance the detection accuracy while keeping great deployment capabilities of a detection model for weld surface defects. To achieve this goal, an improved Yolo-GCH model is proposed based on the stable and fast Yolo-v5. The improvements primarily involve introducing a graph convolution network combined with a self-attention mechanism in the head part (i.e., GCH). This component focuses on improving the insufficient recognition capability of CNN for similar defects in complex environments. Furthermore, to address the presence of potentially ambiguous samples in complex welding environments, the label assignment strategy of simOTA is implemented to optimize the anchor frame. Additionally, a streamlined structure, aiming to improve model detection speed while minimizing performance impact, has been designed to enhance the applicability of the model. The results demonstrate that the cooperation of GCH and simOTA significantly improves the detection performance while maintaining the inference speed. These strategies lead to a 2.5% increase in mAP@0.5 and reduce the missing detection rates of weld and 8 types of defects by 32.9% and 84.1% respectively, surpassing other weld surface detection models. Furthermore, the impressive applicability of the model is verified across four scaled versions of Yolo-v5. Based on the proposed strategies, the FPS increases by more than 30 frames in the fast s and n versions of Yolo-v5. These results demonstrate the great potential of the model for industrial applications.

“…With the ongoing development of computer vision techniques, advanced technologies such as deep learning and machine learning have been widely used for target detection in a variety of industry applications. In particular, classical models, such as faster region convolutional neural networks (Faster R-CNN) [10], single shot multiBox detector (SSD) [11], real-time detection transformer (RT-DETR) [12], and you only look once (YOLO) [13][14][15], have emerged as important methods for detecting lump coal in smart mines. Xue et al [16] used the lightweight network ResNet18 as the backbone feature extraction in YOLOv3.…”

Section: Introductionmentioning

confidence: 99%

SNW YOLOv8: improving the YOLOv8 network for real-time monitoring of lump coal

Wu,

Chen,

et al. 2024

Due to the large size of the coal and the high mining output, lump coal is one of the hidden risks of mining conveyor damage. Typically, lump coal can cause jamming and even damage to the conveyor belt during the coal mining and transportation process. This study proposes a novel real-time detection method for lump coal on a conveyor belt. The Space-to-Depth Conv (SPD-Conv) module is introduced into the feature extraction network to extract the features of the mine's low-resolution lump coal. To enhance the feature extraction capability of the model, the Normalization-based Attention Module (NAM) is combined to adjust weight sparsity. After loss function optimization using the Wise-IoU v3 (WIoU v3) module, the SPD-Conv-NAM-WIoU v3 YOLOv8 (SNW YOLO v8) model is proposed. The experimental results show that the SNW YOLOv8 model outperforms the widely used model (YOLOv8) in terms of precision and recall by 15.82% and 11.71%, respectively. Significantly, the real-time detection speed of the SNW YOLOv8 model is increased to 192.93 f/s. Compared to normal models, the SNW YOLO v8 model overcomes the disadvantages of normal models, such as being overweight, and the parameters of SNW YOLO v8 are reduced to only 6.04 million with a small model volume of 12.3 MB. Meanwhile, the floating point of SNW YOLOv8 is significantly reduced. Consequently, it demonstrates excellent lump coal detection performance, which may open up a new window for coal mining optimization.