Qixiang Tong, Zhipeng Zhu, Min Zhang, Kerui Cao, Haihua Xing^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1353-1375, 2024, DOI:10.32604/cmc.2024.049187

Abstract High-resolution remote sensing image segmentation is a challenging task. In urban remote sensing, the presence of occlusions and shadows often results in blurred or invisible object boundaries, thereby increasing the difficulty of segmentation. In this paper, an improved network with a cross-region self-attention mechanism for multi-scale features based on DeepLabv3+ is designed to address the difficulties of small object segmentation and blurred target edge segmentation. First, we use CrossFormer as the backbone feature extraction network to achieve the interaction between large- and small-scale features, and establish self-attention associations between features at both large and small scales to capture global contextual… 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

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 the original backbone network Xception… More >