Normally functioning and complete printed circuit board (PCB) can ensure the safety and reliability of electronic equipment. PCB defect detection is extremely important in the field of industrial inspection. For traditional methods of PCB inspection, such as contact detection, are likely to damage the PCB surface and have high rate of erroneous detection. In recent years, methods of detection through image processing and machine learning have gradually been put into use. However, PCB inspection is still an extremely challenging task due to the small defects and the complex background. To solve this problem, a lightweight one-stage defect detection network based on dual attention mechanism and Path Aggregation Feature Pyramid Network (PAFPN) has been proposed. At present, some methods of defect detection in industrial applications are often based on object detection algorithms in the field of deep learning. Through comparative experiments, compared with the Faster R-CNN and YOLO v3 which are usually used in the current industrial detection, the inference time of our method are reduced by 17.46 milliseconds (ms) and 4.75 ms, and the amount of model parameters is greatly reduced. It is only 4.42 M, which is more suitable for industrial fields and embedded development systems. Compared with the common one-stage object detection algorithm Fully Convolutional One-Stage Object Detection (FCOS), mean Average Precision (mAP) is increased by 9.1%, and the amount of model parameters has been reduced by 86.12%.

The rapid development of the license plate recognition technology has made great progress for its widespread uses in intelligent transportation system (ITS). This paper has proposed a novel license plate detection and character recognition algorithm based on a combined feature extraction model and BPNN (Backpropagation Neural Network) which is adaptable in weak illumination and complicated backgrounds. Firstly, a preprocessing is first used to strengthen the contrast ratio of original car image. Secondly, the candidate regions of license plate are checked to verify the true plate, and the license plate image is located accurately by the integral projection method. Finally, a new feature extraction model is designed using three sets of features combination, training the feature vectors through BPNN to complete accurate recognition of the license plate characters. The experimental results with different license plate demonstrate effectiveness and efficiency of the proposed algorithm under various complex backgrounds. Compared with three traditional methods, the recognition accuracy of proposed algorithm has increased to 97.7% and the consuming time has decreased to 46.1ms.

The detection of driving events could be useful for reducing accidents, fleet management and insurance premiums etc. Currently, top of the range vehicles and large fleets employ expensive driver monitoring systems. However, most drivers do not have access to such systems. The required monitoring platform would have to deliver the required performance while also being affordable and accessible. A candidate with considerable promise is the smartphone with sensors built-in that could be exploited for the detection of driving events. However, to date it has not been possible to achieve the required correct, missed and false detection rates in addition to the computational efficiency for real-time operations. This paper proposes a novel bagging tree and dynamic time warping (DTW) integrated algorithm for the detection of driving events employing acceleration and orientation data from a smartphone's low cost three-axis accelerometers and gyroscopes. The bagging tree-based machine learning algorithm provides the initial maneuver detection results, as well as the location of the event start and end points. Event detection is then achieved by calculating the similarity of the results predicted through the bagging tree algorithm with the corresponding templates extracted from the experience datasets, while also applying a number of constraints to verify the calculated results. Field test results show that the proposed integrated algorithm is superior to the state-of-the-art, achieving a high correct detection accuracy of 97.5%, a low missed detection of 2.5% and a false detection rate of 2.9%. The corresponding results for the best alternative candidate method are 90.2%, 9.8% and 11.7%. Furthermore, the improvement in computational efficiency offered by our proposed approach is three to more than ten times greater than that of the other state-of-the-art algorithms.

The majority of planning algorithms used are based on the occupancy grid maps, but in complicated situations, the occupancy grid maps have a significant search overhead. This paper proposed a path planner based on the visibility graph (v-graph) for the mobile robot that uses sparse methods to speed up and simplify the construction of the v-graph. Firstly, the complementary grid framework is designed to reduce graph updating iteration costs during the data collection process in each data frame. Secondly, a filter approach based on the edge length and the number of vertices of the obstacle contour is proposed to reduce redundant nodes and edges in the v-graph. Thirdly, a bidirectional breadth-first search is combined into the path searching process in the proposed fast path planner algorithm in order to reduce the waste of exploring space. Finally, the simulation results indicate that the proposed sparse v-graph planner can significantly improve the efficiency of building the v-graph and reduce the time of path search. In highly convoluted unknown or partially known environments, our method is 40% faster than the FAR Planner and produces paths 25% shorter than it. Moreover, the physical experiment shows that the proposed path planner is faster than the FAR Planner in both the v-graph update process and laser process. The method proposed in this paper performs faster when seeking paths than the conventional method based on the occupancy grid.

One of the important factors leading to traffic accidents is the abnormal driving behavior of drivers. Early detection of abnormal driving behaviors can effectively reduce the occurrence of traffic accidents. At present, most of the mainstream driving behavior detection methods are based on the data of a single moment, which separates the continuity of driving behavior. In this paper, a driving behavior recognition algorithm based on Serial-Feature Network (SF-Net) and smart phone inertial sensor is proposed, which fully considers the continuity of driving events and uses adjacent multi time data to identify driving status. The data used in this paper are collected from GPS data, 3-axis acceleration and gyroscope data of smart phone. Through the preprocessing operation, SF-net makes the input vector not only contain the current sensor data, but also fuse the relevant information of adjacent time. In SF-net, deep convolution neural network is used for feature extracting, and 10 different driving behaviors can be identified by fusing multi-level and multi-time feature information. The field test results show that the accuracy rate of the serial feature network is 97.1%, and the recall rate is 98.4%, which is better than other test network models. When the number of training samples is small, the sequential feature network can still maintain a high recognition rate, and the network model is relatively stable.

For ensuring the safety and reliability of electronic equipment, it is a necessary task to detect the surface defects of the printed circuit board (PCB). Due to the smallness, complexity and diversity of minor defects of PCB, it is difficult to identify minor defects in PCB with traditional methods. And the target detection method based on deep learning faces the problem of imbalance between foreground and background when detecting minor defects. Therefore, this paper proposes a minor defect detection method on PCB based on FL-RFCN (focal loss and Region-based Fully Convolutional Network) and PHFE (parallel high-definition feature extraction). Firstly, this paper uses the Region-based Fully Convolutional Network(R-FCN) to identify minor defects on the PCB. Secondly, the focal loss is used to solve the problem of data imbalance in neural networks. Thirdly, the parallel high-definition feature extraction algorithm is used to improve the recognition rate of minor defects. In the detection of minor defects on PCB, the ablation experiment proves that the mean Average accuracy (mAP) of the proposed method is increased by 7.4. In comparative experiments, it is found that the mAP of the method proposed in this paper is 12.3 higher than YOLOv3 and 6.7 higher than Faster R-CNN.