Improvement of Lightweight Convolutional Neural Network Model Based on YOLO Algorithm and Its Research in Pavement Defect Detection.

To ensure the safe operation of highway traffic lines, given the imperfect feature extraction of existing road pit defect detection models and the practicability of detection equipment, this paper proposes a lightweight target detection algorithm with enhanced feature extraction based on the YOLO (You Only Look Once) algorithm. The BIFPN (Bidirectional Feature Pyramid Network) network structure is used for multi-scale feature fusion to enhance the feature extraction ability, and Varifocal Loss is used to optimize the sample imbalance problem, which improves the accuracy of road defect target detection. In the evaluation test of the model in the constructed PCD1 (Pavement Check Dataset) dataset, the mAP@.5 (mean Average Precision when IoU = 0.5) of the BV-YOLOv5S (BiFPN Varifocal Loss-YOLOv5S) model increased by 4.1%, 3%, and 0.9%, respectively, compared with the YOLOv3-tiny, YOLOv5S, and B-YOLOv5S (BiFPN-YOLOv5S; BV-YOLOv5S does not use the Improved Focal Loss function) models. Through the analysis and comparison of experimental results, it is proved that the proposed BV-YOLOv5S network model performs better and is more reliable in the detection of pavement defects and can meet the needs of road safety detection projects with high real-time and flexibility requirements.

A Hybrid Deep Learning Model for Recognizing Actions of Distracted Drivers.

With the rapid spreading of in-vehicle information systems such as smartphones, navigation systems, and radios, the number of traffic accidents caused by driver distractions shows an increasing trend. Timely identification and warning are deemed to be crucial for distracted driving and the establishment of driver assistance systems is of great value. However, almost all research on the recognition of the driver's distracted actions using computer vision methods neglected the importance of temporal information for action recognition. This paper proposes a hybrid deep learning model for recognizing the actions of distracted drivers. Specifically, we used OpenPose to obtain skeleton information of the human body and then constructed the vector angle and modulus ratio of the human body structure as features to describe the driver's actions, thereby realizing the fusion of deep network features and artificial features, which improve the information density of spatial features. The K-means clustering algorithm was used to preselect the original frames, and the method of inter-frame comparison was used to obtain the final keyframe sequence by comparing the Euclidean distance between manually constructed vectors representing frames and the vector representing the cluster center. Finally, we constructed a two-layer long short-term memory neural network to obtain more effective spatiotemporal features, and one softmax layer to identify the distracted driver's action. The experimental results based on the collected dataset prove the effectiveness of this framework, and it can provide a theoretical basis for the establishment of vehicle distraction warning systems.

Multilabel Image Classification Based Fresh Concrete Mix Proportion Monitoring Using Improved Convolutional Neural Network.

Proper and accurate mix proportion is deemed to be crucial for the concrete in service to implement its structural functions in a specific environment and structure. Neither existing testing methods nor previous studies have, to date, addressed the problem of real-time and full-scale monitoring of fresh concrete mix proportion during manufacturing. Green manufacturing and safety construction are hindered by such defects. In this study, a state-of-the-art method based on improved convolutional neural network multilabel image classification is presented for mix proportion monitoring. Elaborately planned, uniformly distributed, widely covered and high-quality images of concrete mixtures were collected as dataset during experiments. Four convolutional neural networks were improved or fine-tuned based on two solutions for multilabel image classification problems, since original networks are tailored for single-label multiclassification tasks, but mix proportions are determined by multiple parameters. Various metrices for effectiveness evaluation of training and testing all indicated that four improved network models showed outstanding learning and generalization ability during training and testing. The best-performing one was embedded into executable application and equipped with hardware facilities to establish fresh concrete mix proportion monitoring system. Such system was deployed to terminals and united with mechanical and weighing sensors to establish integrated intelligent sensing system. Fresh concrete mix proportion real-time and full-scale monitoring and inaccurate mix proportion sensing and warning could be achieved simply by taking pictures and feeding pictures into such sensing system instead of conducting experiments in laboratory after specimen retention.