To address and resolve the issue of visual detection errors due to the different scale of the target and more blockage in complex traffic environments, a multi-scale enhanced traffic scenario target detection algorithm based on YOLOv5 has been proposed. A multi-branch stacking module MSB is designed to replace the C3 module in the backbone network to enhance the feature extraction capability of the backbone network. Based on the structural design of the CSP, the spatial pooling pyramid module is reconstructed, and the SPPFCSP module is designed to replace the SPPF module to obtain more feature information while ensuring the same receptive field. An additional small-scale target detection layer is constructed by fusing the shallow information of the backbone network and the neck network information. A network with shallow information improves the detection capability of small targets. The experimental results show that the improved algorithm`s mAP@0.5 and mAP@0.75 are 91.5% and 61.5% in the KITTI dataset, respectively, which are improved by 3.8% and 6.2% compared with YOLOv5s. The improved algorithm is executed on NVIDIA Jetson AGX Xavier with a real-time detection rate of more than 30 fps, which meets the speed and accuracy requirements of detection algorithms for in-vehicle scenarios.
Aiming at the problems of traditional Grey Wolf Optimization algorithm (GWO), such as easy to fall into local optimization, low precision of optimization, and slow convergence speed, a fast grey wolf optimization algorithm with exponential iterative search is proposed. The tent chaotic mapping method is introduced to realize the diversification of the initial grey wolf population. To balance the global search scope and search speed, an iterative search method for exponential nonlinear control parameters is designed to avoid the algorithm falling into local optimization and accelerate the convergence speed. Finally, an information-sharing method based on the inertia weight coefficient is proposed for the current optimal position of individuals, which dynamically updates the individual step size of grey wolves and improves the speed and accuracy of optimization. Through a variety of standard test functions, compared with the traditional GWO algorithm and three improved algorithms, the performance of the proposed algorithm with fast convergence and strong optimization ability is verified.
In order to improve deficiencies in manual cleaning and lubrication of aero-engine fan blades, such as high working intensity, uneven lubrication, cleaning and lubrication toxic materials, the cleaning and lubrication system of aero-engine fan blades is designed. The system structure, hardware and software control scheme design are completed based on the maintenance work card standard. The cleaning operation can be realized through a combination of mechanical cleaning, ultrasonic cleaning, and the lubrication operation can be completed through industrial robots. The data exchange among the touch screen, mobile robot, PLC, industrial robot and PC five-terminal equipment is realized by using Modbus protocol, and the design of human-computer interface is completed with HMI touch screen. The system can realize automatic cleaning and lubrication operation flow, and avoid direct human contact with cleaning and lubricating toxic solvents. The engine blade cleaning efficiency is 60 s/pc, the lubrication efficiency is 55 s/pc, and the overall operation efficiency is 3.39 times higher than that of manual work. Through actual verification, the system is proved to be able to achieve stable and efficient operation, and has high engineering application value.
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