Two-phase reaction of a two-component molecular crystal of 2-naphthol and 2-naphthylamine suspended in aqueous Fe 3+ solutions gives a cross-coupling product, 2-amino-2A-hydroxy-1,1A-binaphthalene, with good selectivity.

Circle detection is a crucial problem in computer vision and pattern recognition. Improving the accuracy and efficiency of circle detectors has important scientific significance and excellent application value. In this paper, we propose a circle detection method with efficient arc extraction. In order to reduce edge redundancy and eliminate crossing points, we present an edge refinement algorithm to refine the edges into single-pixel-wide branchless contour curves. To address the contour curve segmentation difficulty, we improved the CTAR (Chord to Triangular Arms Ratio) corner detection method to enhance corner point detection and segment the contour curves based on corner points. Then, we used the relative position constraint of arcs to improve the circle detection accuracy further. Finally, we verified the feasibility and reliability of the proposed method by comparing our approach with five other methods using three datasets. The experimental results showed that the presented method had the advantages of anti-obscuration, anti-defect, and real-time performance over other methods.

Aircraft detection in remote sensing images is an important branch of target detection due to the military value of aircraft. However, the diverse categories of aircraft and the intricate background of remote sensing images often lead to insufficient detection accuracy. Here, we present the CNTR-YOLO algorithm based on YOLOv5 as a solution to this issue. The CNTR-YOLO algorithm improves detection accuracy through three primary strategies. (1) We deploy DenseNet in the backbone to address the vanishing gradient problem during training and enhance the extraction of fundamental information. (2) The CBAM attention mechanism is integrated into the neck to minimize background noise interference. (3) The C3CNTR module is designed based on ConvNext and Transformer to clarify the target’s position in the feature map from both local and global perspectives. This module is applied before the prediction head to optimize the accuracy of prediction results. Our proposed algorithm is validated on the MAR20 and DOTA datasets. The results on the MAR20 dataset show that the mean average precision (mAP) of CNTR-YOLO reached 70.1%, which is a 3.3% improvement compared with YOLOv5l. On the DOTA dataset, the results indicate that the mAP of CNTR-YOLO reached 63.7%, which is 2.5% higher than YOLOv5l.

The goal of blast-hole detection is to help place charge explosives into blast-holes. This process is full of challenges, because it requires the ability to extract sample features in complex environments, and to detect a wide variety of blast-holes. Detection techniques based on deep learning with RGB-D semantic segmentation have emerged in recent years of research and achieved good results. However, implementing semantic segmentation based on deep learning usually requires a large amount of labeled data, which creates a large burden on the production of the dataset. To address the dilemma that there is very little training data available for explosive charging equipment to detect blast-holes, this paper extends the core idea of semi-supervised learning to RGB-D semantic segmentation, and devises an ERF-AC-PSPNet model based on a symmetric encoder–decoder structure. The model adds a residual connection layer and a dilated convolution layer for down-sampling, followed by an attention complementary module to acquire the feature maps, and uses a pyramid scene parsing network to achieve hole segmentation during decoding. A new semi-supervised learning method, based on pseudo-labeling and self-training, is proposed, to train the model for intelligent detection of blast-holes. The designed pseudo-labeling is based on the HOG algorithm and depth data, and proved to have good results in experiments. To verify the validity of the method, we carried out experiments on the images of blast-holes collected at a mine site. Compared to the previous segmentation methods, our method is less dependent on the labeled data and achieved IoU of 0.810, 0.867, 0.923, and 0.945, at labeling ratios of 1/8, 1/4, 1/2, and 1.

Circle detection is a fundamental problem in computer vision. However, conventional circle detection algorithms are usually time-consuming and sensitive to noise. In order to solve these shortcomings, we propose a fast circle detection algorithm based on information compression. First, we introduce the idea of information compression, which compresses the circular information on the image into a small number of points while removing some of the noise through sharpness estimation and orientation filtering. Then, the circle parameters stored in the information point are obtained by the average sampling algorithm with a time complexity of O(1) to obtain candidate circles. Finally, we set different constraints on the complete circle and the defective circle according to the sampling results and find the true circle from the candidate circles. The experimental results on the three datasets show that our method can compress the circular information in the image into 1% of the information points, and compared to RHT, RCD, Jiang, Wang and CACD, Precision, Recall, Time and F-measure are greatly improved.