Prof. Quan Zhou Profile

Prof. Quan Zhou

at Nanjing Univ of Posts And Telecommunications

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Proceedings Article | 28 October 2021 Paper

BASNet: improving semantic segmentation via boundary-assistant symmetrical network

Yong Qiang, Quan Zhou, Huiming Shi, Xin Jin, Weihua Ou, Longin Jan Latecki

Proceedings Volume 11884, 1188404 (2021) https://doi.org/10.1117/12.2601128

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Recently, boundary information has gained more attention in improving the performance of semantic segmentation. This paper presents a novel symmetrical network, called BASNet, which contains four components: the pre-trained ResNet-101 backbone, semantic segmentation branch (SSB), boundary detection branch (BDB), and aggregation module (AM). More specifically, our BDB only focuses on processing boundary-related information using a series of spatial attention blocks (SABs). On the other hand, a set of global attention blocks (GABs) are used in SSB to further capture more accurate object boundary information and semantic information. Finally, the outputs of SSB and BDB are fed into AM, which merges the features from SSB and BDB to boost performance. The exhaustive experimental results show that our method not only predicts the boundaries of objects more accurately, but also improves the performance of semantic segmentation.

Proceedings Article | 12 October 2020 Paper

ECDet: an efficient convolutional network for real-time object detection

Jie Wang, Quan Zhou, Dechun Cong, Xin Jin, Weihua Ou

Proceedings Volume 11574, 1157402 (2020) https://doi.org/10.1117/12.2574632

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This paper introduces a lightweight convolutional neural network, called ECDet, for real-time accurate object detection. In contrast to recent advances of lightweight networks that prefer to use pointwise convolution for changing the number of feature map’s channel, ECDet makes an effort to design equal channel block for constructing the whole backbone network architecture. Meanwhile, we deploy depth-wise convolution to compress the feature pyramid network (FPN) detection head. The experiments show that ECDet only has 3.19 M model size and needs only 3.48B FLOPs with a 416×416 input image. Our method has a 5% improvement in accuracy compared to YOLO Nano, and it requires less computation. The comprehensive experiments demonstrate that our model achieves promising results in terms of available speed and accuracy trade-off on PASCAL VOC 2007 datasets.

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