Fan Li^1,2, Shuyao Zhang³, Jie Yang^1,2,*, Zhicheng Feng^1,2, Zhichao Chen^1,2

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3819-3833, 2024, DOI:10.32604/cmc.2024.054525 - 12 September 2024

Abstract Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional (2D) images, such as short detection distance, strong influence of environment and lack of distance information, we propose Rail-PillarNet, a three-dimensional (3D) LIDAR (Light Detection and Ranging) railway foreign object detection method based on the improvement of PointPillars. Firstly, the parallel attention pillar encoder (PAPE) is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder. Secondly, a fine backbone network is designed to improve the feature extraction… More >

Jianwei Yuan¹, Xinli Song^1,*, Huaijian Pu², Zhixiong Zheng³, Ziyang Niu³

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6485-6503, 2023, DOI:10.32604/cmc.2023.035165 - 28 December 2022

Abstract Regular inspection of bridge cracks is crucial to bridge maintenance and repair. The traditional manual crack detection methods are time-consuming, dangerous and subjective. At the same time, for the existing mainstream vision-based automatic crack detection algorithms, it is challenging to detect fine cracks and balance the detection accuracy and speed. Therefore, this paper proposes a new bridge crack segmentation method based on parallel attention mechanism and multi-scale features fusion on top of the DeeplabV3+ network framework. First, the improved lightweight MobileNet-v2 network and dilated separable convolution are integrated into the original DeeplabV3+ network to improve… More >