Mr. Jiangyu Zeng Profile

Jiangyu Zeng

at Univ of Manitoba

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Publications (2)

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Proceedings Article | 22 April 2020 Presentation + Paper

Heat loss detection using thermal imaging by a small UAV prototype

Rahmat Ali, Jiangyu Zeng, Miroslava Kavgic, Young-Jin Cha

Proceedings Volume 11382, 113820S (2020) https://doi.org/10.1117/12.2557902

KEYWORDS: Infrared sensors, Unmanned aerial vehicles, Buildings, Cameras, Thermography, Image sensors, Forward looking infrared, Video, Infrared imaging, Infrared cameras

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There is an increasing interest in detecting heat loss through buildings using unmanned aerial vehicles (UAVs) and thermal sensors. The present study constitutes an attempt to develop a system that can detect heat loss in different inaccessible portions of building structures, such as roofs and high-rise facades. Traditionally, inspectors have conducted surveys to investigate insulation performance and detect heat loss through various portions of buildings. However, these kinds of surveys tend to be time-consuming, costly, and risky. To mitigate risks, a small, low-cost Adafruit thermal infrared sensor and a small, onboard Raspberry Pi microcontroller were mounted on a UAV to detect heat loss through buildings. The lightweight Raspberry Pi microcontroller and Adafruit thermal sensor were powered by additional batteries. A lightweight battery was selected based on the maximum payload and power demand of the microcontroller and thermal sensor. The Raspberry Pi was controlled remotely by a portable computer. The UAV flight plan was controlled remotely by FreeFlight Pro software. Several experimental tests were conducted in both indoor and outdoor environments. Both video and image data were obtained remotely from the thermal sensor and microcontroller. A standard FLIR thermal camera with a very high resolution was also used to ensure the accuracy of the results obtained from the UAV-based thermal sensor. All the images captured by the Adafruit thermal sensor were compared with the standard thermal camera images. The results showed that the presently developed system can detect heat loss through inaccessible locations in buildings with modifications only in sensor resolution.

Proceedings Article | 22 April 2020 Presentation + Paper

Deep learning-based crack detection in a concrete tunnel structure using multispectral dynamic imaging

Rahmat Ali, Jiangyu Zeng, Young-Jin Cha

Proceedings Volume 11382, 1138203 (2020) https://doi.org/10.1117/12.2557900

KEYWORDS: Multispectral imaging, Neural networks, Damage detection, Image processing, Convolution, Cameras, Bridges, Thermography, Network architectures, Alternate lighting of surfaces

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A new computer vision-based method is proposed for concrete crack detection in tunnel structures using multi-spectral dynamic imaging (MSX). The MSX images were collected from a tunnel in the University of Manitoba, Canada. A total of 3600 MSX images (299 × 299 pixels) were used to train the modified deep inception neural network (DINN), and an additional 300 MSX images (299 × 299 pixels) were employed for validation purposes. The MSX images were examined by the trained neural network for concrete crack detection. The main purpose of this research was to examine the potential of the neural network to distinguish between noise and concrete surface cracks in the MSX images. A fully connected layer and a softmax layer were added to the DINN network in the transfer learning section to reduce the network computation cost. The proposed network used green bounding boxes to detect the portions with cracks in the MSX images. A training accuracy of 95.5% and a validation accuracy of 94% were achieved at 1600 iterations. The optimum training steps obtained from the training and validation were used for testing purposes. The robustness of the trained network was evaluated using an additional 96 MSX images (640 × 480 pixels). A maximum testing accuracy of 94% was recorded when the prediction probability was limited to 90%.

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