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-External damage to power facilities caused by crane, excavator and other construction operations increases year by year, which will seriously threaten the safe operation of power system. It is an important measure to ensure the safe and reliable operation of power system to implement intelligent monitoring and early warning of power external breakdown through video and other non-contact observation means. The video data of power mainly comes from the fixed monitoring of helicopters, uavs and transformation poles and towers, which is characterized by large amount of data, complex scenes and serious environmental interference. The traditional target detection method usually selects the candidate area first, and then makes judgment based on the characteristics of human construction. The detection speed is slow and the accuracy is low, which makes it impossible to monitor the video data in real time, so as to make timely and accurate early warning and intervention fbr external damage. The target detection method based on deep learning optimizes or even eliminates the selection of candidate regions, which greatly speeds up the detection speed. By learning a lot of target samples through the deep neural network, the characteristics of high robustness are gradually fitted to make the target judgment more accurate. There are three key problems in introducing the target detection method based on deep learning into the power video detection: Firstly, the target detection method based on deep learning has a large amount of calculation and many parameters. In order to realize in-place operation on terminals with limited computing and storage capacity, it is necessary to find a practical method to simplify the network and reduce the amount of operational data in the detection process, which is the key to realize in-place operation and terminal operation of deep neural network. Secondly, for specific application scenarios, the effect of different target detection algorithms varies greatly, and there is a strong particularity of power video. Finding an effective target detection method is the key to improve the detection speed and accuracy. Finally, with the continuous development of deep learning, the structure of deep neural network changes with each passing day, and each has its own characteristics, which network structure is used as the feature extraction layer of target detection algorithm is the focus of research.

Spot weld layout is critical to structural performance of vehicle and its design is also sub-ject to manufacturing constraints. In this study, using thin-walled tube crash as an exam-ple, we establish the relation between structural performance and weld layout design with manufacturing constraints from resistance spot welding. First, a straight tube crash performance is evaluated as a function of flange width, weld distance to flange corner, and weld pitch, without consideration of manufacturing constraints. All these parameters exhibit certain influence on the deformation mode and the energy absorption capacity. Then, an S-shaped tube is studied in the design optimization of weld layout by adding manufacturing constraints. The proposed approach can determine optimized results by simultaneously considering crash performance and manufacturing constraints. It is also concluded that weld layout has more significant influence on crash performance in straight tubes than in S-shaped tubes. [ABSTRACT FROM AUTHOR]