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    SHREC 2022: Pothole and crack detection in the road pavement using images and RGB-D data
    (Elsevier Ltd, 2022-10-01)
    This paper describes the methods submitted for evaluation to the SHREC 2022 track on pothole and crack detection in the road pavement. A total of 7 different runs for the semantic segmentation of the road surface are compared, 6 from the participants plus a baseline method. All methods exploit Deep Learning techniques and their performance is tested using the same environment (i.e., a single Jupyter notebook). A training set, composed of 3836 semantic segmentation image/mask pairs and 797 RGB-D video clips collected with the latest depth cameras was made available to the participants. The methods are then evaluated on the 496 image/masks pairs in the validation set, on the 504 pairs in the test set and finally on 8 video clips. The analysis of the results is based on quantitative metrics for image segmentation and qualitative analysis of the video clips. The participation and the results show that the scenario is of great interest and that the use of RGB-D data is still challenging in this context.
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    Intelligent Fog Computing Surveillance System for Crime and Vulnerability Identification and Tracing
    (IGI Global, 2023-01-01)
    IoT devices generate enormous amounts of data, which deep learning algorithms can learn from more effectively than shallow learning algorithms. The approach for threat detection may ultimately benefit fog computing or fog networking (fogging). The authors present a cutting-edge distributed DL method for detecting cyberattacks and vulnerability injection (CAVID) in this paper. In terms of the evaluation metrics tested in the tests, the DL model performs better than the SL models. They demonstrated a distributed DL-driven fog computing CAVID approach using the open-source NSLKDD dataset. A pre-trained SAE was utilised for feature engineering, whereas Softmax was employed for categorization. They used parametric evaluation for system assessment to evaluate the model in comparison to SL techniques. For scalability, accuracy across several worker nodes was taken into consideration. In addition to the robustness, effectiveness, and optimization of distributed parallel learning among fog nodes for enhancing accuracy, the findings demonstrate DL models exceeding classic ML architectures.