YOLO (You Only Look Once), as a target detection algorithm with good speed and precision, is widely used in the industry. In the process of driving, the vehicle image captured by the driving camera is detected and it extracts the license plate and the front part of the vehicle. Compared with the network structure of YOLOv3-tiny algorithm, the acquisition method of anchor box is improved by combining the Birch algorithm. In order to improve the real-time performance, the original two-scale detection is added to the multi-scale prediction of three-scale detection to ensure its accuracy. Finally, the experimental results show that the improved YOLOv3-tiny network structure proposed in this study can improve the performance of mean-average-precision, intersection over union and speed by 5.99%, 17.52% and 48.4%, respectively, and the algorithm has certain robustness.How to cite this article: Huang, B., et al.: An improved YOLOv3-tiny algorithm for vehicle detection in natural scenes. IET Cyber-Syst. Robot. 1-9 (2021).

Asynchronous advantage actor-critic (A3C) algorithm is a commonly used policy optimization algorithm in reinforcement learning, in which asynchronous is parallel interactive sampling and training, and advantage is a sampling multi-step reward estimation method for computing weights. In order to address the problem of low efficiency and insufficient convergence caused by the traditional heuristic exploration of A3C algorithm in reinforcement learning, an improved A3C algorithm is proposed in this paper. In this algorithm, a noise network function, which updates the noise tensor in an explicit way is constructed to train the agent. Generalised advantage estimation (GAE) is also adopted to describe the dominance function. Finally, a new mean gradient parallelisation method is designed to update the parameters in both the primary and secondary networks by summing and averaging the gradients passed from all the sub-processes to the main process. Simulation experiments were conducted in a gym environment using the PyTorch Agent Net (PTAN) advanced reinforcement learning library, and the results show that the method enables the agent to complete the learning training faster and its convergence during the training process is better. The improved A3C algorithm has a better performance than the original algorithm, which can provide new ideas for subsequent research on reinforcement learning algorithms. K E Y W O R D S asynchronous advantage actor-critic (A3C), generalised advantage estimation (GAE), parallelisation, reinforcement learningThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

An improved Ghost-YOLOv5s detection algorithm is proposed in this paper to solve the problems of high computational load and undesirable recognition rate in the traditional detection methods of pavement diseases. Ghost modules and C3Ghost are introduced into the YOLOv5s network to reduce the FLOPs (floating-point operations) in the feature channel fusion process. Mosaic data augmentation is also added to improve the feature expression performance. A public road disease dataset is reconstructed to verify the performance of the proposed method. The proposed model is trained and deployed to NVIDIA Jetson Nano for the experiment, and the results show that the average accuracy of the proposed model reaches 88.17%, increased by 4.01%, and the model FPS (frames per second) reaches 12.51, increased by 184% compared with the existing YOLOv5s. Case studies show that the proposed method satisfies the practical application requirements of pavement disease detection.