Computer vision methods have raised great interest in the manufacture of printed circuit boards (PCBs). The detection and localization of circular holes on PCBs are very important for positioning or defect detection. In this paper, a new method for such detection is presented. The proposed method first uses an adaptive edge detector to obtain the edge image. Then it utilizes the symmetry of the circle and transforms the PCB image into distance space where a Euclidean distance relationship is shown. Then a rapid circle center search (RCCS) algorithm is used to locate the center of each circle. This algorithm first scans the possible locations of each center and then filters out false selections by measuring how close the candidate center is to the contour with the Hausdorff distance. In addition, the proposed method is compared with the method based on the circular Hough transform for multiple test images in terms of speed and accuracy.

Lakes play an important role in the water ecosystem on earth, and are vulnerable to climate change and human activities. Thus, the detection of water quality changes is of great significance for ecosystem assessment, disaster warning and water conservancy projects. In this paper, the dynamic changes of the Poyang Lake are monitored by Synthetic Aperture Radar (SAR). In order to extract water from SAR images to monitor water change, a water extraction algorithm composed of texture feature extraction, feature fusion and target segmentation was proposed. Firstly, the fractal dimension and lacunarity were calculated to construct the texture feature set of a water object. Then, an iterated function system (IFS) was constructed to fuse texture features into composite feature vectors. Finally, lake water was segmented by the multifractal spectrum method. Experimental results showed that the proposed algorithm accurately extracted water targets from SAR images of different regions and different imaging modes. Compared with common algorithms such as fuzzy C-means (FCM), the accuracy of the proposed algorithm is significantly improved, with an accuracy of over 98%. Moreover, the proposed algorithm can accurately segment complex coastlines with mountain shadow interference. In addition, the dynamic analysis of the changes of the water area of the Poyang Lake Basin was carried out with the local hydrological data. It showed that the extracted results of the algorithm in this paper are a good match with the hydrological data. This study provides an accurate monitoring method for lake water under complex backgrounds.

It is difficult to obtain a large number of high‐quality labelled Synthetic Aperture Radar (SAR) image data. In order to solve the classification task of an SAR image with limited data, this paper proposes a dual‐network classification model of pre‐training and fine‐tuning based on contrastive learning and pseudo‐label training strategy. The algorithm firstly obtains the initial weight of the network through unsupervised pre‐training and then takes advantage of the pseudo‐label information to realise the fine tuning of the dual network. It effectively reduces the number of samples required during classification. Compared with the classification network without optimisation strategy, the proposed algorithm achieves higher classification accuracy in the ablation study.

This paper introduces a novel method for road target segmentation in the context of unmanned driving based on stereo disparity maps. The proposed method utilizes topological persistence threshold analysis to address the challenges associated with selecting appropriate threshold values. The approach involves converting 3D vehicle road images into uv-disparity maps, extracting road planes using v-disparity maps, and calculating occupancy grid maps using u-disparity maps. Persistence diagrams are then constructed by generating segmentation results under various threshold parameters. By establishing persistence boundaries in these diagrams, the most significant regions are identified, enabling the determination of robust segmentation thresholds. Experimental validation using KITTI stereo image datasets demonstrates the effectiveness of the proposed method, with low error rates and superior performance compared to other thresholding segmentation methods. The research holds potential for application in autonomous driving systems.

Due to obstructions in the natural environment, practical emotion recognition applications often experience reduced recognition accuracy. In order to address this technological challenge, this paper proposes an emotion recognition approach embedding adaptive dual-channel attention, specifically designed for recognizing facial expressions in obscured faces. This method employs an adaptive dual-channel attention mechanism to focus on the uncovered facial feature areas, enhancing the algorithm's capability to concentrate on features. Additionally, it utilizes a dual-stream residual multi-scale network to augment perception of features at various scales, thus improving recognition accuracy. Experimental evaluations conducted on the AffectNet dataset and SFEW 2.0 dataset demonstrate that this approach yields superior recognition accuracy compared to existing methods. This research provides an effective avenue for enhancing user experience in applications such as human-computer interaction.

In addressing the path planning challenges encountered by Automated Guided Vehicles (AGVs) within smart warehousing transportation and storage, as well as the inherent issues of traditional A* algorithm, we propose an optimized and enhanced A* algorithm for path optimization. Building upon the conventional algorithm, the enhanced A* algorithm selects appropriate heuristic functions and weighting coefficients, integrating Bezier curves to achieve the final improved A* algorithm. The heuristic function is used to optimize the problem of long paths. Combined with Bezier curve, the path tortuous problem that occurs in the process of path planning is solved, and the path is smoother. The environmental description is conducted using a grid map constructed in Matlab, enabling simulation analysis of the algorithm within this grid-based environment. The simulation results reduce the path length by approximately 18.7% and the run time by 15.0%. The results analysis demonstrates that the enhanced A* algorithm not only effectively avoids obstacles but also identifies a better path. The enhanced A* algorithm yields shorter paths. Consequently, the enhanced A* algorithm proves to be a more accurate and efficient path planning technique, showcasing superior global search capabilities.