Wen-Yi Peng - Academia.edu (original) (raw)

Papers by Wen-Yi Peng

OCEANS 2021: San Diego – Porto

There is little work done for underwater saliency objection detection (SOD), but it is vital to a... more There is little work done for underwater saliency objection detection (SOD), but it is vital to artificial intelligence-driven underwater analysis. Recent research has shown that depth information would increase SOD accuracy, but it may not be accessible to most RGB datasets. Since image blurriness could be an estimate of underwater scene depth [1], we propose to use a self-derived blurriness cue and fuse it into the RGB stream to boost SOD accuracy. Experimental results demonstrate the effectiveness of the proposed method. Our work would also contribute a public underwater SOD dataset to the field of underwater SOD.

IEEE Signal Processing Letters

When we shoot pictures through transparent media, such as glass, reflection can undesirably occur... more When we shoot pictures through transparent media, such as glass, reflection can undesirably occur, obscuring the scene we intended to capture. Therefore, removing reflection is practical in image restoration. However, a reflective scene mixed with that behind the glass is challenging to be separated, considered significantly ill-posed. This letter addresses the single image reflection removal (SIRR) problem by proposing a knowledge-distilling-based content disentangling model that can effectively decompose the transmission and reflection layers. The experiments on benchmark SIRR datasets demonstrate that our method performs favorably against state-of-the-art SIRR methods.

2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), 2021

Most underwater image restoration works indicate that restoring images can improve their visual q... more Most underwater image restoration works indicate that restoring images can improve their visual quality and performance of computer vision tasks that use the restored data. This paper aims to investigate how image restoration contributes to underwater object detection. We found that utilizing image restoration through data augmentation rather than using it as a pre-processing step can boost underwater object detection accuracy (up to a 1.9% gain in mAP) on URPC2019 dataset.

Applied Sciences, 2020

Street lighting is a fundamental aspect of security systems in homes, industrial facilities, and ... more Street lighting is a fundamental aspect of security systems in homes, industrial facilities, and public places. To detect parking lot occupancy in outdoor environments, street light control plays a crucial role in smart surveillance applications that can perform robustly in extreme surveillance environments. However, traditional parking occupancy systems are mostly implemented for outdoor environments using costly sensor-based techniques. This study uses the Jetson TX2 to develop a method that can accurately identify street parking occupancy and control streetlights to assist occupancy detection, thereby reducing costs, and can adapt to various weather conditions. The proposed method adopts You Only Look Once version 3 (YOLO v3, Seattle, WA, USA) based on MobileNet version 2 (MobileNet v2, Salt Lake City, UT, USA), which is area-based and uses voting to stably recognize occupancy status. This solution was verified using the CNRPark + EXT dataset, a simulated model, and real scenes p...

OCEANS 2021: San Diego – Porto

There is little work done for underwater saliency objection detection (SOD), but it is vital to a... more There is little work done for underwater saliency objection detection (SOD), but it is vital to artificial intelligence-driven underwater analysis. Recent research has shown that depth information would increase SOD accuracy, but it may not be accessible to most RGB datasets. Since image blurriness could be an estimate of underwater scene depth [1], we propose to use a self-derived blurriness cue and fuse it into the RGB stream to boost SOD accuracy. Experimental results demonstrate the effectiveness of the proposed method. Our work would also contribute a public underwater SOD dataset to the field of underwater SOD.

IEEE Signal Processing Letters

When we shoot pictures through transparent media, such as glass, reflection can undesirably occur... more When we shoot pictures through transparent media, such as glass, reflection can undesirably occur, obscuring the scene we intended to capture. Therefore, removing reflection is practical in image restoration. However, a reflective scene mixed with that behind the glass is challenging to be separated, considered significantly ill-posed. This letter addresses the single image reflection removal (SIRR) problem by proposing a knowledge-distilling-based content disentangling model that can effectively decompose the transmission and reflection layers. The experiments on benchmark SIRR datasets demonstrate that our method performs favorably against state-of-the-art SIRR methods.

2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), 2021

Most underwater image restoration works indicate that restoring images can improve their visual q... more Most underwater image restoration works indicate that restoring images can improve their visual quality and performance of computer vision tasks that use the restored data. This paper aims to investigate how image restoration contributes to underwater object detection. We found that utilizing image restoration through data augmentation rather than using it as a pre-processing step can boost underwater object detection accuracy (up to a 1.9% gain in mAP) on URPC2019 dataset.

Applied Sciences, 2020

Street lighting is a fundamental aspect of security systems in homes, industrial facilities, and ... more Street lighting is a fundamental aspect of security systems in homes, industrial facilities, and public places. To detect parking lot occupancy in outdoor environments, street light control plays a crucial role in smart surveillance applications that can perform robustly in extreme surveillance environments. However, traditional parking occupancy systems are mostly implemented for outdoor environments using costly sensor-based techniques. This study uses the Jetson TX2 to develop a method that can accurately identify street parking occupancy and control streetlights to assist occupancy detection, thereby reducing costs, and can adapt to various weather conditions. The proposed method adopts You Only Look Once version 3 (YOLO v3, Seattle, WA, USA) based on MobileNet version 2 (MobileNet v2, Salt Lake City, UT, USA), which is area-based and uses voting to stably recognize occupancy status. This solution was verified using the CNRPark + EXT dataset, a simulated model, and real scenes p...