Unmanned aerial vehicle visual inspection and recognition method for crane complex steel structure surface defects
The invention discloses an unmanned aerial vehicle visual inspection and recognition method for crane complex steel structure surface defects, which comprises the following steps: carrying a high-resolution visible light camera by using an inverted unmanned aerial vehicle platform, and collecting im...
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| creator | WU XIAOYUE CAO MING ZHANG SHENRU FENG YUEGUI JIANG MING NI DAJIN ZHOU QIANFEI WANG SHUANG NING SHIXIANG DING SHUQING WU XIANGSHENG WANG HUIFANG QING GUANGWEI |
| description | The invention discloses an unmanned aerial vehicle visual inspection and recognition method for crane complex steel structure surface defects, which comprises the following steps: carrying a high-resolution visible light camera by using an inverted unmanned aerial vehicle platform, and collecting images through a cattle-tilling type full-coverage inspection path; constructing a classification algorithm fusing a support vector machine, a deep convolutional network and a generative adversarial network, subjecting multi-scale and multi-type defects on the surface of the crane structure under the complex background to classification detection, and marking the positions of the defects with a minimum bounding rectangular frame of the defects; segmenting the detected defect target frame area to extract a connected domain, establishing a pixel equivalent accurate calibration model based on multi-point laser ranging, and counting and collecting parameters such as length, width and area of the defect through a pixel po |
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constructing a classification algorithm fusing a support vector machine, a deep convolutional network and a generative adversarial network, subjecting multi-scale and multi-type defects on the surface of the crane structure under the complex background to classification detection, and marking the positions of the defects with a minimum bounding rectangular frame of the defects; segmenting the detected defect target frame area to extract a connected domain, establishing a pixel equivalent accurate calibration model based on multi-point laser ranging, and counting and collecting parameters such as length, width and area of the defect through a pixel po</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HANDLING RECORD CARRIERS IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
| title | Unmanned aerial vehicle visual inspection and recognition method for crane complex steel structure surface defects |
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