<p>Non-motor vehicles are widely used in the urban and rural transportation system for their portability, but the related violations also occur frequently and are difficult to be supervised intelligently, considering their colossal quantity, various styles, and small volumes. To solve this problem, this paper presents a non-motor vehicle violation detection algorithm with efficient target detection and deliberate logical calculation. A target detection network with high speed and accuracy is constructed firstly by fusing two different types of attention mechanism. Specifically, the Squeeze-and-Excitation Network is employed to optimize the extraction of local features, which can effectively reduce the error rate of target detection. Meanwhile, the Transformer Network is adopted to strengthen the extraction of global features, which can improve the target location performance for target tracking in high-density scenarios. As the global and local features are integrated with attention mechanism, the proposed network can accurately identify and locate the numerous small targets in real-time, and avoid identity switching caused by target occlusion. Finally, the violation of non-motor vehicles is recognized in real-time by constructing the logical calculation between the target features and their motion trajectories. Experiments on datasets show that the detection accuracy of the proposed algorithm is better than the current mainstream algorithms, especially the accuracy of small targets such as Head and Helmet is higher than 92.2%. And our ID Switch is dropped by more than 60% compared with the classical Deep SORT algorithm. In real-life scenarios, the proposed algorithm also shows excellent accuracy and real-time performance for non-motor vehicle violations.</p> <p>&nbsp;</p>

<p>Vehicle detection is one of the key techniques of intelligent transportation system with high requirements for accuracy and real-time. However, the existing algorithms suffer from the contradiction between detec-tion speed and detection accuracy, and weak generalization ability. To address these issues, an improved vehicle detection algorithm is presented based on the You Only Look Once (YOLO). On the one hand, an efficient feature extraction network is restructured to speed up the feature transfer of the object, and re-use the feature information extracted from the input image. On the other hand, considering that the fewer pixels are occupied for the smaller objects, a novel feature fusion network is designed to fuse the seman-tic information and representation information extracted by different depth feature extraction layers, and ultimately improve the detection accuracy of small and medium objects. Experiment results indicate that the mean Average Precision (mAP) of the proposed algorithm is up to 93.87%, which is 11.51%, 18.56% and 20.42% higher than that of YOLOv3, CornerNet, and Faster R-CNN, respectively. Furthermore, its detection speed can meet the real-time requirement of practical application basically with 49.45 frames per second.</p> <p>&nbsp;</p>

<p>High-efficiency video coding (HEVC) has improved the coding performance by 50% compared with the previous H.264 coding standard. However, it has also introduced an extremely high coding complexity. The quad-tree partition used by the coding unit (CU) is one of the key factors leading to the increase in complexity. Therefore, this paper proposes a CU partition method based on a convolutional neural net-work (CNN). Aiming at the complex recursive calculation of CU partition, an improved VGGNet network structure is proposed to replace the brute-force search strategy, which effectively reduces the computa-tional complexity of intra frame coding. Finally, to enhance the effectiveness of the network model in this paper, the feature pyramid network is added to the CNN model to improve the accuracy of feature extraction. The experimental results show that the proposed method can reduce the intra coding time by 59.71% while maintaining the coding performance.</p> <p>&nbsp;</p>