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Scratches and cracks in steel severely affect its service life and performance. However, owing to the irregular shapes and sizes of steel surface defects, defects within the same class may be different, whereas defects between classes may be similar. Existing methods focus only on spatial information, resulting in low detection accuracy. To alleviate these problems, this paper proposes the ECDY (EIFEM CARAFE DyHead) network to enhance the detection capability of steel defects. We first design a feature extraction module that focuses on the edge information of feature contours. This module uses the Sobel operator to extract the edge information of a feature and fuses it with the overall spatial information so that richer semantic information can be obtained. The module has improved accuracy in the YOLOv5, YOLOv8, and YOLOv10 versions, and uses fewer parameters and calculations. In particular, in YOLOv8x, mAP@0.5 increased by 2.5%, and the number of parameters was reduced by 12.4 M. Second, to retain the detailed information in the feature pyramid, and to better reconstruct features, we choose the content-aware reassembly feature method (CARAFE) as the upsampling method. Finally, the detection head was replaced with a dynamic unified detection head (DyHead) to adapt to different defect sizes and different task requirements. Compared with YOLOv8s, the proposed method improves precision by 1.6%, recall by 4%, and mAP@0.5 by 4%. This value is 4.2% higher than the mAP@0.5 of the current SOTA model RT-DETR-L in the field of object detection and has 23.2 M fewer parameters.

The wide variety of crops in the image of agricultural products and the confusion with the surrounding environment information makes it difficult for traditional methods to extract crops accurately and efficiently. In this paper, an automatic extraction algorithm is proposed for crop images based on Mask RCNN. First, the Fruits 360 Dataset label is set with Labelme. Then, the Fruits 360 Dataset is preprocessed. Next, the data are divided into a training set and a test set. Additionally, an improved Mask RCNN network model structure is established using the PyTorch 1.8.1 deep learning framework, and path aggregation and features are added to the network design enhanced functions, optimized region extraction network, and feature pyramid network. The spatial information of the feature map is saved by the bilinear interpolation method in ROIAlign. Finally, the edge accuracy of the segmentation mask is further improved by adding a micro-fully connected layer to the mask branch of the ROI output, employing the Sobel operator to predict the target edge, and adding the edge loss to the loss function. Compared with FCN and Mask RCNN and other image extraction algorithms, the experimental results demonstrate that the improved Mask RCNN algorithm proposed in this paper is better in the precision, Recall, Average precision, Mean Average Precision, and F1 scores of crop image extraction results.

As a classic and effective edge detection operator, the Sobel operator has been widely used in image segmentation and other image processing technologies. This operator has obvious advantages in the speed of extracting the edge of images, but it also has the disadvantage that the detection effect is not ideal when the image contains noise. In order to solve this problem, this paper proposes an optimized scheme for edge detection. In this scheme, the weighted nuclear norm minimization (WNNM) image denoising algorithm is combined with the Sobel edge detection algorithm, and the excellent denoising performance of the WNNM algorithm in a noise environment is utilized to improve the anti-noise performance of the Sobel operator. The experimental results show that the optimization algorithm can obtain better detection results when processing noisy images, and the advantages of the algorithm become more obvious with the increase of noise intensity.

With the continuous advancement of unmanned aerial vehicles (UAVs) and computer vision technologies, UAV-based insulator defect detection has become a crucial approach to ensuring the safety of power systems. However, this task still faces multiple challenges, such as scale imbalance, blurred edges, and complex backgrounds. To address these issues, this paper proposes a Gated Unified Reparameterized Large Kernel Network (GURLKNet) to enhance insulator defect detection performance. Specifically, a Gated Unified Reparameterized Large Kernel Module (GUR-LKM) is designed to suppress redundant channels through a gating mechanism and introduce partial depthwise convolution structures, which significantly expand the receptive field. Furthermore, an Edge-Guided Feature Stem (EGFStem) is constructed by integrating the Sobel edge operator with a texture-guided mechanism to strengthen shallow features’ perception of structural boundaries. In addition, a Context-Interactive Fusion Network (CIFNet) is introduced, employing a multi-scale attention-guided strategy to alleviate semantic inconsistency and improve the semantic expression and localization accuracy of feature fusion. The experimental results on several insulator defect datasets show that the proposed method demonstrates strong overall accuracy while maintaining low computational cost, and outperforms mainstream object detection models on most evaluation metrics. Compared to the baseline model, GURLKNet achieves a mAP50 improvement of 3.5% on the Insulator-DET dataset and 0.9% on the IDID dataset. This study provides an efficient and reliable solution for intelligent insulator inspection, promoting the engineering application and deployment of object detection technology in low-altitude power system sensing.

The medical image processing has become indispensable with an increased demand for systematic and efficient detection of brain tumor in a short period of time. There are various techniques for medical image segmentation. Detecting a wide variety of brain images in terms of shape and intensity is a challenging and difficult task to bring out a reliable and authentic data for diagnosing brain tumor diseases. This paper presents an algorithm which combines Region of Interest (ROI), Region Growing and Morphological Operation (Dilation and Erosion). This method initially identifies the approximate Region Growing (RG). Region growing is a procedure that groups pixels into larger regions, which starts from the seed points. Region growing based techniques are better than the edge-based techniques in noisy images where edges are difficult to detect. The Morphological Edge Detection of the input image is done and the input image is reconstructed on the basis of dilation and erosion for the enhancement of the image. The proposed work is divided into preprocessing to reduce the noise, Fuzzy C-Means is used to Region growing, Morphological edge detection is to enhance the image. Then the morphological edge detection can be classified into two categories, one is dilation and another is Erosion. Finally apply Gaussian filter to get output. After that, Fuzzy C-Means clustering (FCM), followed by seeded region growing is applied to detect and segment the tumor from the brain MRI image.

The primary impediments in lung CT image segmentation stem from the ambiguity in edge definition and the inadequate segmentation accuracy. Addressing these issues, this paper introduces a novel composite lung CT image segmentation framework that integrates an attention mechanism with an edge detection operator. We utilize residual dynamic convolutions as the encoder to augment the network's capability for extracting and representing nuanced lesion features. Sobel edge detection is integrated into the skip connections to facilitate the transmission and utilization of edge information. In particular, we introduce an information fusion attention module for deeper layers, optimizing feature reorganization and utilization by attention mechanisms and dilated convolution. Experimental evaluations on two lung CT datasets reveal that our proposed AE-UNet achieves outstanding segmentation performance, surpassing the best baseline network by an average of 0.93%.

The art of abstraction reflects the rich imagination of human beings to a certain extent and is now widely used in movies, games, advertisements, and other modern design fields. In this paper, a new edge tangential flow algorithm applicable to image abstraction is proposed, which first estimates the gradient strength and gradient direction of each point in a given input image by using the Sobel operator and then establishes the edge graph of the input image by using the edge information transfer strategy. On this basis, the application and value of abstract art and this graphical abstract processing method in modern graphic design are discussed, and the essence of abstract form art is perfectly embodied so as to make the two perfectly integrated. The results show that in the application of graphic abstract processing in graphic design, the average value of user attraction, understanding, and memory dimension scores are 0.53, 0.675, and 0.683, respectively, which verifies the effectiveness of this graphic design practice through users.

In this paper, a novel adaptive threshold Sobel edge detection algorithm based on the improved genetic algorithm is proposed to detect edges. Because of the influence of external factors in actual detection process, the result of detection is often not accurate enough when the configured threshold of the target image is far away from the real threshold. Different thresholds of images are calculated by improved genetic algorithm for different images. The calculated threshold is used in edge detection. The experimental results show that the image processed by the improved algorithm has stronger edge continuity. It is shown that proposed algorithm has a better detection effect and applicability than the traditional Sobel algorithm.

An Otsu-threshold- and Canny-edge-detection-based fast Hough transform (FHT) approach to lane detection was proposed to improve the accuracy of lane detection for autonomous vehicle driving. During the last two decades, autonomous vehicles have become very popular, and it is constructive to avoid traffic accidents due to human mistakes. The new generation needs automatic vehicle intelligence. One of the essential functions of a cutting-edge automobile system is lane detection. This study recommended the idea of lane detection through improved (extended) Canny edge detection using a fast Hough transform. The Gaussian blur filter was used to smooth out the image and reduce noise, which could help to improve the edge detection accuracy. An edge detection operator known as the Sobel operator calculated the gradient of the image intensity to identify edges in an image using a convolutional kernel. These techniques were applied in the initial lane detection module to enhance the characteristics of the road lanes, making it easier to detect them in the image. The Hough transform was then used to identify the routes based on the mathematical relationship between the lanes and the vehicle. It did this by converting the image into a polar coordinate system and looking for lines within a specific range of contrasting points. This allowed the algorithm to distinguish between the lanes and other features in the image. After this, the Hough transform was used for lane detection, making it possible to distinguish between left and right lane marking detection extraction; the region of interest (ROI) must be extracted for traditional approaches to work effectively and easily. The proposed methodology was tested on several image sequences. The least-squares fitting in this region was then used to track the lane. The proposed system demonstrated high lane detection in experiments, demonstrating that the identification method performed well regarding reasoning speed and identification accuracy, which considered both accuracy and real-time processing and could satisfy the requirements of lane recognition for lightweight automatic driving systems.

Defect detection plays a crucial role in chip quality control, and the chip field has widely researched and applied machine vision-based surface defect detection methods due to their high efficiency, accuracy, and real-time performance. In this paper, we utilize imaging equipment to collect images of high-performance phase noise measurement chips, and we use a mean filtering algorithm and a Sobel operator to preprocess the collected chip images. Then, the PCA method is applied to downscale the extracted chip shape and texture features, and the improved support vector machine algorithm using a genetic algorithm is used to classify and recognize chip defect features. The test results show that the error rate of the defect detection method for high-performance phase noise measurement of chip surface defects is only 1.82% at the highest, and the average error of the measurement of the chip pin width and pitch is much lower than the actual production of the specified error rate. Meanwhile, the design requirement of 3 pcs/s detection efficiency in the actual production of high-performance phase noise measurement chips is satisfied by the theoretical maximum defect detection efficiency of the method. The chip defect detection method presented in this paper has both practical application value and theoretical research significance.