Abstract
As an important transportation hub, the detection of appearance defects in bridges has been characterized by low accuracy and low efficiency. To address this problem, the study proposes a bridge appearance defect recognition model based on image processing and improved convolutional neural network. The model is divided into three different modules. The first module is the bridge appearance defect classification and recognition module based on transfer learning and convolutional network. The second module further localizes the region of defective cracks based on the classification and recognition results. This module uses improved fast region convolutional neural network for region segmentation to further determine the location of cracks in the image. Finally, operations such as corrosion and expansion are performed on the cracks through morphological theory to further extract the crack size information. The results indicated that the detection accuracy, missed detection rate, false detection rate, response time, and size calculation accuracy of the proposed appearance defect recognition model were 98.2%, 0.6%, 0.5%, 1.9s, and 97.8%, respectively. Compared with the previous method, the positioning accuracy of the improved method is increased by 5.46%, and the area under the receiver operating curve is increased by 0.11. It can be concluded that the proposed appearance defect detection and identification model can realize a more refined defect identification, which in turn provides a reliable basis for the routine maintenance and health condition monitoring of bridges.