Search Results (57)

To rectify significant heading calculation errors in polarized light navigation for unmanned aerial vehicles (UAVs) under tilted states, this paper proposes a method for compensating horizontal attitude angles based on horizon detection. First, a defogging enhancement algorithm that integrates Retinex theory with dark channel prior is adopted to improve image quality in low-illumination and hazy environments. Second, a dynamic threshold segmentation method in the HSV color space (Hue, Saturation, and Value) is proposed for robust horizon region extraction, combined with an improved adaptive bilateral filtering Canny operator for edge detection, aimed at balancing detail preservation and noise suppression. Then, the progressive probabilistic Hough transform is used to efficiently extract parameters of the horizon line. The calculated horizontal attitude angles are utilized to convert the body frame to the navigation frame, achieving compensation for polarization orientation errors. Onboard experiments demonstrate that the horizontal attitude angle estimation error remains within 0.3°, and the heading accuracy after compensation is improved by approximately 77.4% relative to uncompensated heading accuracy, thereby validating the effectiveness of the proposed algorithm. Full article

Due to the construction damage of high-density polyethylene film (HDPE) during the early stages of landfill construction and missed or faulty welding, this paper proposes a method based on the synchronous characteristic temperature differences between defective and intact areas of HDPE film. An image feature-edge-picking algorithm was used to detect various defects. First, under the action of a continuous heat source, infrared images of different types of defects on the surface of HDPE films were collected, and we recorded the temperature of different areas on the film surface. We also analyzed the changes in the temperatures of the complete and defect areas over time and extracted the temperature characteristic curves. Second, the contour characteristics of hidden defects in the weld area were analyzed. The image with the most substantial temperature difference resolution was selected and preliminary noise reduction was performed. Further enhancement of the edges was carried out using the guided image-filtering (GIF) algorithm, which was improved by using the edge-aware weighting in weighted guided image filtering (WGIF) and the weighted aggregation mechanism in weighted aggregated guided image filtering (WAGIF). Finally, the Canny operator was used to detect the edges of the processed images to recognize the contour of the welding defect. The best pixel image was extracted, the pixel comparison relationship was used to quantitatively detect the defect size of the HDPE film and the error between the image defect size and the actual size was analyzed. The experimental results show that the model could identify the surface defects on HDPE film during construction and could obtain the approximate outline and size of the hidden defects in the welding area. Full article

The automotive brake piston component is an important part of the automotive brake system, and the concentricity detection of the first piston component is crucial to ensure driving safety. In this paper, an improved Canny algorithm is proposed for non-contact detection of spring concentricity of the first piston component. Firstly, the traditional Canny algorithm is improved by replacing the Gaussian filter with a bilateral filter to fully retain the edge information, and accurate edge detection results are obtained by constructing a multi-scale analysis. After obtaining the edge images, a sub-pixel edge detection method with gray moments is introduced to optimize these edges; secondly, a circle is fitted to the extracted edge points by using the RANSAC algorithm to determine the center position and radius of the circle; and finally, the concentricity of the first piston part is calculated based on the fitting results. The experimental results are compared with those of the CMM and the traditional Canny algorithm, and the results show that the improved Canny algorithm reduces the coaxiality error by 4% and enables effective measurement of the concentricity of the first piston assembly spring. Full article

Background: Ocular diseases have been a severe problem worldwide, specifically in underdeveloped countries that do not have enough technology or economy to treat them. It would be beneficial to have software with low installation complexity and ease of use, allowing high efficacy in diagnosing eye diseases. This study aims to design and implement an algorithm based on deep learning to classify ocular diseases with high precision. Methods: This work describes digital image processing techniques for the easier handling of eye images; in particular, blur filters were used. The Canny filter was also applied to obtain the edges that allow the difference between the analyzed diseases. Once the images were pre-processed, a convolutional neural network of our own design was applied to perform the classification task. The validation algorithm used in this work was the hold-out algorithm (80–20). The metrics used to evaluate our proposal were the confusion matrix, accuracy, recall precision, and F1-score. Results: The dataset has five classes, namely, normal, cataract, diabetic retinopathy, glaucoma, and other retina diseases. The network architecture consists of 11 layers, including three convolutional layers, three max pooling layers, one batch normalization layer, one flattening layer, two hidden layers, and one output layer. This model resulted in 97% efficiency across all metrics. Conclusions: With the individual analysis of each metric, it can be observed that the proposed algorithm is capable of differentiating, first, images of healthy eyes from diseased ones and, second, adequately classifying eye diseases. Full article

In this paper, we present a novel color edge detection method that integrates collaborative filtering with multiscale gradient fusion. The Block-Matching and 3D (BM3D) filter is utilized to enhance sparse representations in the transform domain, effectively reducing noise. The multiscale gradient fusion technique compensates for the loss of detail in single-scale edge detection, thereby improving both edge resolution and overall quality. RGB images from the dataset are converted into the XYZ color space through mathematical transformations. The Colored Block-Matching and 3D (CBM3D) filter is applied to the sparse images to reduce noise. Next, the vector gradients of the color image and anisotropic Gaussian directional derivatives for two scale parameters are computed. These are then averaged pixel-by-pixel to generate a refined edge strength map. To enhance the edge features, the image undergoes normalization and non-maximum suppression. This is followed by edge contour extraction using double-thresholding and a novel morphological refinement technique. Experimental results on the edge detection dataset demonstrate that the proposed method offers robust noise resistance and superior edge quality, outperforming traditional methods such as Color Sobel, Color Canny, SE, and Color AGDD, as evidenced by performance metrics including the PR curve, AUC, PSNR, MSE, and FOM. Full article

In agricultural production, lettuce growth, yield, and quality are impacted by nutrient deficiencies caused by both environmental and human factors. Traditional nutrient detection methods face challenges such as long processing times, potential sample damage, and low automation, limiting their effectiveness in diagnosing and managing crop nutrition. To address these issues, this study developed a lettuce nutrient deficiency detection system using multi-dimensional image analysis and Field-Programmable Gate Arrays (FPGA). The system first applied a dynamic window histogram median filtering algorithm to denoise captured lettuce images. An adaptive algorithm integrating global and local contrast enhancement was then used to improve image detail and contrast. Additionally, a multi-dimensional image analysis algorithm combining threshold segmentation, improved Canny edge detection, and gradient-guided adaptive threshold segmentation enabled precise segmentation of healthy and nutrient-deficient tissues. The system quantitatively assessed nutrient deficiency by analyzing the proportion of nutrient-deficient tissue in the images. Experimental results showed that the system achieved an average precision of 0.944, a recall rate of 0.943, and an F1 score of 0.943 across different lettuce growth stages, demonstrating significant improvements in automation, accuracy, and detection efficiency while minimizing sample interference. This provides a reliable method for the rapid diagnosis of nutrient deficiencies in lettuce. Full article

The real-time measurement of head warping and lower buckling during the production process of rough-rolled slabs has long been a persistent technical problem at the production site. Currently, the detection of head warping and lower buckling in the production site relies on workers’ operational experience for manual observation or measurement during machine downtime. In this paper, an online real-time detection system for the head warping and lower buckling of rough-rolled slab in hot continuous rolling based on visual detection is proposed, and a cascade filter based on morphological processing is developed, which can effectively remove the noise in the field environment and smooth the edge profile of the slab. A precise measurement and analysis method based on points and lines is proposed, which determines the precise values by subtracting the distance from the corner-point at the top of slab to the straight line at its lower edge from that between its upper and lower edges. The detection system in industrial applications has demonstrated high accuracy: detection error ≤ ±5 mm, type recognition rate ≥ 99%. Meeting on-site industrial production requirements. Full article

A method for the conversion of SAR (Synthetic Aperture Radar) images to optical images can be useful for disaster area detection for the following two reasons: (1) it is easier to detect disaster areas with optical images rather than with SAR images; (2) disasters may occur at night and in rainy and cloudy conditions (SAR images can be acquired in daytime and nighttime as well as all weather conditions). Therefore, it becomes easier to detect disaster areas with optical images converted from SAR images. Using GANs (Generative Adversarial Networks), it is possible to convert SAR images to optical images. In particular, pix2pix and pix2pixHD are used for this purpose. The author proposed spatial resolution-maintained pix2pixHD previously. In this paper, a new method of modifying pix2pixHD with a spatial attention mechanism and an edge enhancement mechanism with a Canny filter in the loss function is proposed, and the proposed method is compared to the pix2pixHD with a spatial attention mechanism and pix2pixHD as well as pix2pix. All of these four methods are compared in terms of the spatial resolution (frequency components) of converted optical images. By experiment, the superiority of the modified pix2pixHD with spatial attention and edge enhancement mechanisms is confirmed for disaster area detection (landslide area detection). Full article

Visual positioning accuracy is crucial for ensuring the successful execution of nut disassembly and assembly tasks by a fastener-nut disassembly and assembly robot. However, disturbances such as on-site lighting changes, abnormal surface conditions of nuts, and complex backgrounds formed by ballast in complex railway environments can lead to poor visual positioning accuracy of the fastener nuts, thereby affecting the success rate of the robot’s continuous disassembly and assembly operations. Additionally, the existing method of detecting fasteners first and then positioning nuts has poor applicability in the field. A direct positioning algorithm for spiral rail spikes that combines an improved Canny algorithm with shape feature similarity determination is proposed in response to these issues. Firstly, CLAHE enhances the image, reducing the impact of varying lighting conditions in outdoor work environments on image details. Then, to address the difficulties in extracting the edges of rail spikes caused by abnormal conditions such as water stains, rust, and oil stains on the nuts themselves, the Canny algorithm is improved through three stages, filtering optimization, gradient boosting, and adaptive thresholding, to reduce the impact of edge loss on subsequent rail spike positioning results. Finally, considering the issue of false fitting due to background interference, such as ballast in gradient Hough transformations, the differences in texture and shape features between the rail spike and interference areas are analyzed. The HOG is used to describe the shape features of the area to be screened, and the similarity between the screened area and the standard rail spike template features is compared based on the standard Euclidean distance to determine the rail spike area. Spiral rail spikes are discriminated based on shape features, and the center coordinates of the rail spike are obtained. Experiments were conducted using images collected from the field, and the results showed that the proposed algorithm, when faced with complex environments with multiple interferences, has a correct detection rate higher than 98% and a positioning error mean of 0.9 mm. It exhibits excellent interference resistance and meets the visual positioning accuracy requirements for robot nut disassembly and assembly operations in actual working environments. Full article

The traditional SIFT (Scale Invariant Feature Transform) registration algorithm is highly regarded in the field of image processing due to its scale invariance, rotation invariance, and robustness to noise. However, it faces challenges such as a large number of feature points, high computational demand, and poor real-time performance when dealing with large-scale images. A novel optimization method based on integral image technology and canny edge detection is presented in this paper, aiming to maintain the core advantages of the SIFT algorithm while reducing the complexity involved in image registration computations, enhancing the efficiency of the algorithm for real-time image processing, and better adaption to the needs of large-scale image handling. Firstly, Gaussian separation techniques were used to simplify Gaussian filtering, followed by the application of integral image techniques to accelerate the construction of the entire pyramid. Additionally, during the feature point detection phase, an innovative feature point filtering strategy was introduced by combining Canny edge detection with dilation operations alongside the traditional SIFT approach, aiming to reduce the number of feature points and thereby lessen the computational load. The method proposed in this paper takes 0.0134 s for Image type a, 0.0504 s for Image type b, and 0.0212 s for Image type c. In contrast, the traditional method takes 0.1452 s for Image type a, 0.5276 s for Image type b, and 0.2717 s for Image type c, resulting in reductions of 0.1318 s, 0.4772 s, and 0.2505 s, respectively. A series of comparative experiments showed that the time taken to construct the Gaussian pyramid using our proposed method was consistently lower than that required by the traditional method, indicating greater efficiency and stability regardless of image size or type. Full article

This study presents a novel method for automatically analyzing archery shooting time using AI and computer vision technologies, with a particular focus on the critical anchoring to release phase, which directly influences performance. The proposed approach detects the start of the anchoring phase using pose estimation and accurately measures the shooting time by detecting the bowstring within the athlete’s facial bounding box, utilizing Canny edge detection and the probabilistic Hough transform. To ensure stability, low-pass filtering was applied to both the facial bounding box and pose estimation results, and an algorithm was implemented to handle intermittent bowstring detection due to various external factors. The proposed method was validated by comparing its results with expert manual measurements obtained using Dartfish software v2022 achieving a mean absolute error (MAE) of 0.34 s and an $R^2$ score of 0.95. This demonstrates a significant improvement compared to the bowstring-only method, which resulted in an MAE of 1.4 s and an $R^2$ score of 0.89. Previous research has demonstrated a correlation between shooting time and arrow accuracy. Therefore, this method can provide real-time feedback to athletes, overcoming the limitations of traditional manual measurement techniques. It enables immediate technical adjustments during training, which can contribute to overall performance improvement. Full article

Droplet quality in drop-on-demand (DoD) Electrohydrodynamic (EHD) inkjet printing plays a crucial role in influencing the overall performance and manufacturing quality of the operation. The current approach to droplet printing analysis involves manually outlining/labeling the printed dots on the substrate under a microscope and then using microscope software to estimate the dot sizes by assuming the dots have a standard circular shape. Therefore, it is prone to errors. Moreover, the dot spacing information is missing, which is also important for EHD DoD printing processes, such as manufacturing micro-arrays. In order to address these issues, the paper explores the application of feature extraction methods aimed at identifying characteristics of the printed droplets to enhance the detection, evaluation, and delineation of significant structures and edges in printed images. The proposed method involves three main stages: (1) image pre-processing, where edge detection techniques such as Canny filtering are applied for printed dot boundary detection; (2) contour detection, which is used to accurately quantify the dot sizes (such as dot perimeter and area); and (3) centroid detection and distance calculation, where the spacing between neighboring dots is quantified as the Euclidean distance of the dot geometric centers. These stages collectively improve the precision and efficiency of EHD DoD printing analysis in terms of dot size and spacing. Edge and contour detection strategies are implemented to minimize edge discrepancies and accurately delineate droplet perimeters for quality analysis, enhancing measurement precision. The proposed image processing approach was first tested using simulated EHD printed droplet arrays with specified dot sizes and spacing, and the achieved quantification accuracy was over 98% in analyzing dot size and spacing, highlighting the high precision of the proposed approach. This approach was further demonstrated through dot analysis of experimentally EHD-printed droplets, showing its superiority over conventional microscope-based measurements. Full article

In the process of laser-based direct energy deposition (DED-LB), the quality of the deposited layer will be affected by the process parameters and the external environment, and there are problems such as poor stability and low accuracy. A molten pool monitoring method based on coaxial vision is proposed. Firstly, the molten pool image is captured by a coaxial CCD camera, and the geometric features of the molten pool are accurately extracted by image processing techniques such as grayscale, median filtering noise reduction, and K-means clustering combined with threshold segmentation. The molten pool width is accurately extracted by the Canny operator combined with the minimum boundary rectangle method, and it is used as the feedback of weld pool control. The influence of process parameters on the molten pool was further analyzed. The results show that with an increase in laser power, the width and area of the molten pool increase monotonously, but exceeding the material limit will cause distortion. Increasing the scanning speed will reduce the size of the molten pool. By comparing the molten pool under constant power mode and width control mode, it is found that in width control mode, the melt pool width fluctuates less, and the machining accuracy is improved, validating the effectiveness of the real-time control system. Full article

Crack detection is significant to building repair and maintenance; however, conventional inspection is a labor-intensive and time-consuming process for field engineers. This paper proposes a three-step computer vision-based framework to quickly recognize concrete cracks and automatically identify their length, maximum width, and area in damage images. In step one, a region-based convolutional neural network (YOLOv8) is applied to train the crack localizing model. In step two, Gaussian filtering, Canny, and FindContours are integrated to extract the reference contour (a pre-designed seal) to obtain the conversion scale between pixels and millimeter-wise sizes. In step three, the recognized crack bounding box is cropped, and the ApproxPolyDP function and Hough transform are performed to quantify crack dimensions based on the conversion ratio. The developed framework was validated on a dataset of 4630 crack images, and the model training took 150 epochs. Results show that the average crack detection accuracy reaches 95.7%, and the precision of quantified dimensions is over 90%, while the error increases as the crack size grows smaller (increasing to 8% when the crack width is within 1 mm). The proposed method can help engineers to efficiently achieve crack information at building inspection sites, while the reference frame must be pre-marked near the crack, which may limit the scope of application scenarios. In addition, the robustness and accuracy of the developed image processing techniques-based crack quantification algorithm need to be further improved to meet the requirements in real cases when the crack is located within a complex background. Full article

This work presents a novel approach to enhancing iris recognition systems through a two-module approach focusing on low-level image preprocessing techniques and advanced feature extraction. The primary contributions of this paper include: (i) the development of a robust preprocessing module utilizing the Canny algorithm for edge detection and the circle-based Hough transform for precise iris extraction, and (ii) the implementation of Binary Statistical Image Features (BSIF) with domain-specific filters trained on iris-specific data for improved biometric identification. By combining these advanced image preprocessing techniques, the proposed method addresses key challenges in iris recognition, such as occlusions, varying pigmentation, and textural diversity. Experimental results on the Human-inspired Domain-specific Binarized Image Features (HDBIF) Dataset, consisting of 1892 iris images, confirm the significant enhancements achieved. Moreover, this paper offers a comprehensive and reproducible research framework by providing source codes and access to the testing database through the Notre Dame University dataset website, thereby facilitating further application and study. Future research will focus on exploring adaptive algorithms and integrating machine learning techniques to improve performance across diverse and unpredictable real-world scenarios. Full article