Defect detection holds significant importance in improving the overall quality of fabric manufacturing. To improve the effectiveness and accuracy of fabric defect detection, we propose the PRC-Light YOLO model for fabric defect detection and establish a detection system. Firstly, we have improved YOLOv7 by integrating new convolution operators into the Extended-Efficient Layer Aggregation Network for optimized feature extraction, reducing computations while capturing spatial features effectively. Secondly, to enhance the performance of the feature fusion network, we use Receptive Field Block as the feature pyramid of YOLOv7 and introduce Content-Aware ReAssembly of FEatures as upsampling operators for PRC-Light YOLO. By generating real-time adaptive convolution kernels, this module extends the receptive field, thereby gathering vital information from contexts with richer content. To further optimize the efficiency of model training, we apply the HardSwish activation function. Additionally, the bounding box loss function adopts the Wise-IOU v3, which incorporates a dynamic non-monotonic focusing mechanism that mitigates adverse gradients from low-quality instances. Finally, in order to enhance the PRC-Light YOLO model’s generalization ability, we apply data augmentation techniques to the fabric dataset. In comparison to the YOLOv7 model, multiple experiments indicate that our proposed fabric defect detection model exhibits a decrease of 18.03% in model parameters and 20.53% in computational load. At the same time, it has a notable 7.6% improvement in mAP.

PRC-Light YOLO: An Efficient Lightweight Model for Fabric Defect Detection

A pavement crack identification method based on an improved C-mask region-based convolutional neural network (R-CNN) model is proposed to solve problems whereby existing crack recognition algorithms exhibit low accuracy and cannot perform detection and segmentation tasks simultaneously. The crack dataset that was collected in this study included three categories: transverse cracks, longitudinal cracks, and alligator cracks. The model integrates the detection task and the segmentation task into one model, and segments the crack pixels in the generated detection box while achieving target positioning. Firstly, based on the mask R-CNN model, the improved C-mask R-CNN method is designed, which improves the quality of the region proposal box by combining the detectors that are cascaded with different intersections over union thresholds, and achieves accurate crack location under high-threshold detection. Secondly, the ratio of the anchor in the model is adjusted, and a series of optimization parameters and experimental comparisons are carried out for the improved model to realize the segmentation of the crack pixels in the generated detection box during the crack location. The effectiveness of the proposed model is verified, and finally, an evaluation method for cracks is proposed. Furthermore, the calculation of the crack geometric parameters is completed. The experimental results demonstrate that the mean average precision (mAP) of the C-mask R-CNN model detection part reached 95.4%, and the mAP of the segmentation part reached 93.5%. Moreover, the proposed model is convenient for researchers to deploy and implement.

Automatic Pavement Crack Identification Based on an Improved C-Mask Region-Based Convolutional Neural Network Model

This paper presents a textile defect detection method that utilizes a multi‐proportion spatial attention mechanism and channel memory feature fusion network by addressing the difficulties presented by complicated shapes and large size variations. In particular, a multi‐proportion spatial attention mechanism (MPAM) is introduced, which employs multi‐proportion convolution to improve the backbone network's capacity to detect non‐uniform structural defects. Additionally, the generality and adaptability of the model are enhanced by a multi‐scale spatial pyramid pooling structure (MS‐SPP). Second, a channel attention mechanism‐based memory feature fusion network is developed, which incorporates channel attention to adaptive weight the feature channels, focusing on crucial information channels to efficiently fuse contextual features and enhance the model's memory capacity. Finally, a novel efficient Wise‐IoU (EWIoU) loss function is proposed, which utilizes a dynamic non‐monotonic focusing mechanism to increase the penalty on distance measurement, thus enhancing the model's detection performance. Experiment findings on the ZJU‐Leaper and Tianchi textile datasets reveal that compared to the YOLOv7 baseline, the method in this paper has an increase of 6.5 and 2 percentage points, respectively, and the detection accuracy is better than most existing networks.

Textile defect detection based on multi‐proportion spatial attention mechanism and channel memory feature fusion network

Object detection is a fundamental challenge encountered in understanding and analyzing remote sensing images. Many current detection methods struggle to identify objects in remote sensing images due to poor feature saliency, diverse directions, and unique viewing angles of small remote sensing targets. As most remote sensing images are captured through overhead photography, the structural contour features of objects in these images remain relatively stable, such as the cross-shaped structure of the aircraft and the rectangular structure of the vehicle. Therefore, we utilize the physical simulation images as prior knowledge to supplement the invariant structural features of the target, extract the target's key geometric features through the dynamic matching method, and fuse it with the features extracted by the neural network to detect the remote sensing small target more effectively in this article. We propose a template matching method based on physical simulation images (TMSI) and add it to the modified Darknet-53 (named TMSI-Net) for small target detection. Based on this, we perform specific transformations on existing templates in terms of target scale and direction to achieve better adaptation to the corresponding goals and propose DTMSI-Net with a dynamic template library (Dynamic TMSI-Net). Experiments on several datasets demonstrate that the DTMSI-Net exhibits higher detection accuracy and more stable performance compared with the state-of-the-art methods, 4% and 2.7% higher than the second-ranked model on the VEDAI and NWPU datasets, respectively. 

Physical-Simulation-based Dynamic Template Matching Method for Remote Sensing Small Object Detection

Defect detection plays a pivotal role in quality control for fabrics. In order to enhance the accuracy and efficiency of fabric defect detection, we have proposed the PRC-Light YOLO model for fabric defect detection and have established a detection system. Firstly, we add new convolution operators in Backbone for the YOLOv7 and integrate them with Extended-Efficient Layer Aggregation Network. This combination constructs a new feature extraction module that not only reduces the computational effort of the network model, but also extracts spatial features effectively. Secondly, we employ multi-branch dilated convolutions feature pyramid, and introduce lightweight upsampling operators to improve performance of the feature fusion network. This module achieves an expanded receptive field by generating real-time adaptive convolution kernels, allowing for the collection of crucial information from regions with a richer contextual context. To further minimize the computation during network model training, we adopt the HardSwish activation function. Finally, we apply the Wise-IOU v3 bounding box loss function as a dynamic non-monotonic focusing mechanism, which decreases adverse gradients from low-quality instances. We conduct data augmentation on real fabric dataset to raise the generalization capability of the PRC-Light YOLO model. Compared with the YOLOv7 model, numerous simulation experiments show that our proposed methods reduce the model’s parameters and computation by 18.03% and 20.53%, respectively. Simultaneously, there has been a 7.6% improvement in mAP. 

In order to solve the problem of defect detection and to contour accurate segmentation of periodic texture fabric images, a fabric defect detection method based on saliency region and similarity location is proposed. Firstly, the image to be detected was processed by color space conversion, Gaussian filtering, and contrast enhancement, and a frequency-tuned (FT) salient region detection algorithm was used to estimate a saliency map of the enhanced image. The fabric image was divided into image blocks of the same size with overlapping areas through a sliding window, and then the statistical parameters of each image block were calculated. The outliers in the statistical parameters were filtered out using inter-quartile range (IQR). Through the positioning and processing of image defects, abnormal elimination was carried out, and the defect outline was finally obtained. The experimental results show that the method proposed in this paper has better performance in terms of qualitative characterization of Acc, Precision, Recall, and F1 score.

/pdf/fabric-defect-detection-algorithm-based-on-image-saliency-2d42a00s.pdf

Fabric Defect Detection Algorithm Based on Image Saliency Region and Similarity Location

Object detection from remote sensing images is an interesting research topic full of theoretical and application values. Compared with generic images, those of remote sensing contain much less distinguishing representation information due to far-away capturing position and limited camera lens resolution, the perpendicular to ground capturing view, almost unacquirable 3-D object structure and serious roof occlusion, and the inherent unimportance of top roof view. Focusing on this intrinsic disadvantage, in this letter, we propose a general framework of accurate remote sensing object localization constructed with the core idea of maximum image feature exploration. This integrated structure is mainly comprised of cascaded tiny patch correlation (CTPC) based feature digging, averaging local patch regression (ALPR) centered coarse location acquisition, and instance segmentation oriented further refinement (ISOFR). All those components are designed to maximize feature digging and usage. We believe this is the first to implement the idea of maximum feature exploration to each corner of object detection architecture. Extensive experiments on challenging aerial image datasets DOTA and NWPU VHR-10 show state-of-the-art performance.

A Framework of Maximum Feature Exploration Oriented Remote Sensing Object Detection

Cracks are the most common diseases of highway pavement, which affect the safe operation of highway. Therefore, timely and effective detection and maintenance of cracks in highway pavement is essential. The existing pavement crack detection methods are mainly based on the condition that the image quality is good, the pavement is clean, and the image has no shadow. But in the actual situation, the background of the pavement image is complex. Therefore, it is necessary to conduct in-depth research on pavement crack detection under complex background. The pavement cracks under complex background are characterized by uneven illumination, more interference noise and weak crack information. In this paper, a pavement crack image enhancement algorithm based on luminance region is proposed, which is used to eliminate the shadows on the pavement image, and then the maximum entropy threshold segmentation algorithm is used to extract the cracks on the pavement image. The experimental results show that the algorithm can accurately detect cracks on shadowed pavement.

Research on pavement crack detection algorithm in complex background

In response to the problems of weak segmentation performance, insufficient precision, and poor robustness of current road crack segmentation methods in complex environments, this paper proposes an improved UNet network structure for road segmentation. In order to better extract the characteristics of road cracks, this paper adds a residual module to the backbone network of UNet. In order to adjust attention, this article added the CBAMC3 module to the neck network. On the open source dataset EdmCrack600, this model was compared with the original model and ablation experiments were conducted on the optimized model. The experimental results show that the optimized model in this article has stronger generalization ability and performance compared to the original model.

A Road Crack Segmentation Algorithm Based on Attention Mechanism

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Papers

Fabric Defect Detection Algorithm Based on Image Saliency Region and Similarity Location

A Framework of Maximum Feature Exploration Oriented Remote Sensing Object Detection

Research on pavement crack detection algorithm in complex background

A Road Crack Segmentation Algorithm Based on Attention Mechanism