Vehicle object detection in aerial scenes has important applications in both military and civilian fields. Recently, deep learning has shown clear advantages in object detection, and the detection performance has been continuously improved. However, these deep object detection algorithms rely on anchor-based approaches accompanied by complex convolutional operations. In this paper, we establish a lightweight aerial vehicle object detection algorithm based on the method of anchor-free. The anchor-free based object detection method effectively gets rid of the limitation of detection model capability by the size of fixed anchor box, which reduces the set of parameters and provides a more flexible solution space. In addition, the proposed lightweight object feature extraction network effectively reduces the computational cost of the model, while improving the feature extraction capability of small objects. Besides, we use channel stacking to improve the object feature extraction capability of the lightweight network, and introduce the attention mechanism in the detection model to improve the efficiency of resource utilization. We evaluate the proposed detection algorithm on both the public aerial dataset and our collected aerial dataset, and the results show that our algorithm has significant advantages over other detection algorithms in detection accuracy and efficiency. The proposed detection algorithm achieves 89.1% and 92.6% mAP on the Munich dataset and the created dataset, and the detection time for each image is 1.21s and 0.036s, respectively.

Even though the deep neural networks have strong feature representation capability and high recognition accuracy in finger vein recognition, the deep models are computationally intensive and poor in timeliness. To address these issues, this article proposes a lightweight algorithm for finger vein image recognition and matching. The proposed algorithm uses a lightweight convolutional model in the backbone network and employs a triplet loss function to train the model, which not only improves the matching accuracy, but also satisfies the real-time matching requirements. In addition, the Mini-region of interest (RoI) and finger vein pattern feature extraction also effectively solve the problems of large amounts of calculation and background noise. Moreover, the present model recognizes new categories based on the feature vector space constructed by the finger vein recognition system, so that new categories can be recognized without retraining the model. The results show that the finger vein recognition and matching algorithm proposed in this article achieves 99.3% and 99.6% in recognition accuracy and 14.2 and 16.5 ms in matching time for the dataset Shandong University Machine Learning and Applications Laboratory-Homologous Multimodal Biometric Traits (SDUMLA-HMT) and Peking University Finger Vein Dataset (PKU-FVD), respectively. These metrics show that our approach is time-saving and more effective than previous algorithms. Compared with the state-of-the-art finger vein recognition algorithm, the proposed algorithm improves 1.45% in recognition accuracy while saving 45.7% in recognition time.

The detection of traffic objects in aerial scenes holds significant application potential in both military and civilian sectors. However, current aerial traffic object detection techniques based on computer vision face challenges including limited awareness of object direction, a heavy computational burden on the feature extraction backbone network, and inadequate capacity to learn crucial semantic information. In this paper, our focus is on investigating the mechanisms for predicting the directional perception of traffic objects in aerial scenes, achieving backbone network lightness, and exploring methods for extracting key semantic information from objects. Firstly, to tackle the challenge of poor perception of traffic object direction and angle in aerial scenes, we utilize techniques like equivariant vector field convolution, multi-task anchor-free prediction, and adaptive loss to develop a precise mechanism for recognizing and predicting object directions. Secondly, given the presence of small-sized and numerous objects in aerial scenes, we propose the adoption of a lightweight backbone network employing channel stacking to decrease the model’s computational burden. Additionally, we establish a theoretical framework and methodology for optimizing and compressing this backbone network, aimed at enhancing feature extraction and propagation for aerial traffic objects. Furthermore, to address the issue of inadequate learning of key semantic information features, we incorporate saliency attention and multi-scale contextual information to capture the essential semantic characteristics of the objects. We also establish a method for extracting semantic features specifically for aerial traffic objects. The approach presented in this paper broadens the applicability of aerial object detection algorithms and offers novel methodologies and theoretical foundations for object detection in intricate scenarios.

Instruments detect and respond to various parameters, playing a crucial role in civil and industrial applications. However, the current instrument recognition algorithm still suffers from low detection accuracy, a large amount of model calculation, and a low degree of automatic recognition. In this article, we propose an efficient real-time automatic instrument indication acquisition algorithm based on a lightweight deep convolutional neural network and hybrid attention fine-grained features. In this algorithm, we divide instrument identification into three subtasks, which are instrument object detection, instrument fine-grained recognition, and instrument indication acquisition. In the instrument object detection task, we propose a lightweight instrument detection algorithm, which uses a lightweight feature extraction backbone network and a single-scale object detection algorithm to reduce the computational load. In the instrument fine-grained recognition task, this article proposes a fine-grained classification network based on attention hybrid clipping, which can effectively recognize the instrument fine-grained category and range features. In the instrument indication acquisition task, the edge detection and polar coordinate change are used to realize an automated instrument identification and reading system, which can meet the needs of high-precision real-time monitoring of the instrument indication in various complex scenarios. In addition, we have created and made publicly available an instrument image dataset that contains 3457 images of various types of instruments in actual scenes. The recognition accuracy of the instrument reading recognition algorithm proposed in this article is 92.9% on the created dataset, and the average recognition time per image is 348 ms on the i7-8700 CPU.

With the rapid development of the electronic industry, the defect detection of printed circuit board (PCB) components is becoming more and more important. The types of PCB components are diverse and accompanied by complex character information, which is difficult to identify. The traditional detection method is inefficient, and it is unable to effectively perform the diversified category detection of PCB components and character recognition in complex scenes. The deep convolutional neural network has obvious advantages in object detection and character recognition, which can be used to implement a PCB component defect detection system. In this study, the authors have established a lightweight PCB type detection model called LD‐PCB, which can perform real‐time detection while improving detection accuracy. In addition, in the character detection of PCB, they have established a fast and robust character recognition model, called CR‐PCB. This model can effectively improve the accuracy of irregular character recognition. Finally, they established and published a dataset of PCB components, and combined with LD‐PCB and CR‐PCB to realise the PCB defect detection system. This system can realise the functions of defect detection, wrong insertion, missing insertion, and character recognition in industrial PCB production. The results show that the method proposed in this study can effectively detect defects on PCB components.