Delivery of online electronics and mechatronics labs during lockdowns
- Authors: Jayawardena, Amal , Kahandawa, Gayan , Piyathilaka, Lasitha
- Date: 2021
- Type: Text , Conference paper
- Relation: 8th IEEE International Conference on e-Learning in Industrial Electronics, ICELIE 2021, Virtual, Toronto, 13-16 October 2021, Proceedings - 2021 8th IEEE International Conference on e-Learning in Industrial Electronics, ICELIE 2021
- Full Text: false
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- Description: This paper provides a detailed explanation of several approaches that can be used to conduct online labs for elec-tronics/mechatronics engineering courses and explains the results obtained from a survey conducted. The detailed explanations provide information on how to implement the method, benefits of the stated process, possible challenges, and how to overcome those challenges. Furthermore, this paper presents the analyzed results from a survey conducted to capture the student experience in online labs. © 2021 IEEE.
Real-time concrete crack detection and instance segmentation using deep transfer learning
- Authors: Piyathilaka, Lasitha , Preethichandra, Daluwathu , Izhar, Umer , Appuhamillage, Gayan
- Date: 2020
- Type: Text , Journal article
- Relation: Engineering Proceedings Vol. 2, no. 1 (2020), p.
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- Description: Cracks on concrete infrastructure are one of the early indications of structural degradation which needs to be identified early as possible to carry out early preventive measures to avoid further damage. In this paper, we propose to use YOLACT: a real-time instance segmentation algorithm for automatic concrete crack detection. This deep learning algorithm is used with transfer learning to train the YOLACT network to identify and localize cracks with their corresponding masks which can be used to identify each crack instance. The transfer learning techniques allowed us to train the network on a relatively small dataset of 500 crack images. To train the YOLACT network, we created a dataset with ground-truth masks from images collected from publicly available datasets. We evaluated the trained YOLACT model for concrete crack detection with ResNet-50 and ResNet-101 backbone architectures for both precision and speed of detection. The trained model achieved high mAP results with real-time frame rates when tested on concrete crack images on a single GPU. The YOLACT algorithm was able to correctly segment multiple cracks with individual instance level masks with high localization accuracy.