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Regularly detect the corrosion of metal structures and take countermeasures according to the degree of corrosion, which can reduce the potential safety hazards and avoid unnecessary economic losses. This paper presents a deep learning-based metal corrosion detection method, which is used to accurately segment the corrosion regions. This model incorporates Hybrid Attention Residual Block (HARB) and External Skip Connections (ESC) in the basic architecture of U-Net. HARB can reweight the features so that the network pays more attention to the corrosion regions and ignores other irrelevant regions. Shallow features and deep features are fused using ESC to make the features of network learning more effective and comprehensive, which is conducive to improving the segmentation accuracy. In order to further improve the semantic segmentation performance, the Attention Gate Residual Block (AGRB) is proposed, which can optimize segmentation by strengthening the regions to be segmented and reducing the activation value of the background, while overcoming the semantic and scale inconsistencies between input features. In this paper, comprehensive comparative experiments are conducted on the Metal Corrosion dataset. The experimental results show that the F1 and mIoU of the proposed method are 91.21% and 89.56%, respectively. Compared with U-Net, HEU-Net achieves a 7.91% improvement in F1 and a 10.16% improvement in mIoU. These results show that our method outperforms state-of-the-art models and can achieve better semantic segmentation results. [ABSTRACT FROM AUTHOR]

Unlike 2D images that are represented in regular grids, 3D point clouds are irregular and unordered, hence directly applying convolution neural networks (CNNs) to process point clouds is quite challenging. In this paper, we propose a novel deep neural network named AKNet to achieve point cloud semantic segmentation. The key to our AKNet is the attentive kernel convolution (AKConv), which is a deformed convolution operation for perceiving sufficient local context of 3D scenes. AKConv first constructs the Basic Weight Units that are robust to point's ordering. Then, for capturing the more distinctive local features, the convolution kernels of AKConv are associated with Attentive Weight Units through the self-attentive function acting on Basic Weight Units. Furthermore, 3D point clouds provide richer geometric shape information, which is helpful to recognize objects. However, inputting only raw point features to the convolution function could cause geometric information loss. Thus, we utilize augmented features as input of AKConv. Besides, to preserve the semantic information from the encoding to decoding layers, we introduce the backward encoding (BE) mechanism by utilizing higher-layer semantic features. We conduct experiments on three large-scale point clouds datasets. The experimental results demonstrate that our AKNet outperforms state-of-the-art (SOTA) networks. [ABSTRACT FROM AUTHOR]

Three-dimensional object detection plays a key role in autonomous driving, which becomes extremely challenging in occlusion situations. This paper presents a novel multimodal 3D object detection framework which fuses visual semantic information and depth point cloud information to accurately detect targets with distant object features and occlusion situations. The framework consists of the four steps. Firstly, an improved semantic segmentation network is used to extract semantic information of objects containing similar features. Secondly, semantic images and point clouds are combined to generate pixel-level fusion data so that the semantic information and training capability of sparse and far-point clouds can be improved. Thirdly, a deep learning-based point cloud classification network is used for training of the fused data to output accurate detection frames. Fourthly, an extended Kalman filter is incorporated into point cloud prediction for image-based object detection to further enhance the robustness of object detection. Both Cityscapes and KITTI datasets are used in ablation study and experiments to validate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Semantic segmentation plays a crucial role in the fields of computer vision and computer graphics, with extensive applications in various practical scenarios. Significant progress has been made in semantic segmentation tasks using deep learning-based methods. However, most existing semantic segmentation algorithms focus on good weather conditions, and they face challenges in terms of accuracy and robustness when applied to rainy scenes due to factors such as raindrops, haze, and lighting variations. To address this issue, this paper proposes a rainy-day semantic segmentation method based on a two-stage progressive network. The proposed method consists of two modules: a rain removal module responsible for eliminating raindrops and haze from the input rainy images and restoring the basic structural information of the images, and a segmentation module that performs pixel-level semantic prediction on the rain-removed images. Specifically, the rain removal module introduces two progressive units with shared weights to gradually achieve rain removal. The segmentation module adopts an encoder–decoder architecture, utilizing down-sampling and deep asynchronous bottleneck units for encoding. It also introduces a dual attention-guided fusion module to aggregate channel attention information and spatial attention information, guiding the multiscale feature fusion process in the decoder. Experimental results demonstrate that this method effectively mitigates the influence of rain streaks on semantic segmentation, thereby improving segmentation performance and achieving more accurate and robust semantic segmentation results in rainy conditions. We will provide the code and datasets on https://github.com/zhang152267/TSPN. [ABSTRACT FROM AUTHOR]