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Kin-Man Lam

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4 papers

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AAAI Conference 2024 Conference Paper

AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection

Jingchun Zhou
Zongxin He
Kin-Man Lam
Yudong Wang
Weishi Zhang
Chunle Guo
Chongyi Li

In this paper, we present a novel Amplitude-Modulated Stochastic Perturbation and Vortex Convolutional Network, AMSP-UOD, designed for underwater object detection. AMSP-UOD specifically addresses the impact of non-ideal imaging factors on detection accuracy in complex underwater environments. To mitigate the influence of noise on object detection performance, we propose AMSP Vortex Convolution (AMSP-VConv) to disrupt the noise distribution, enhance feature extraction capabilities, effectively reduce parameters, and improve network robustness. We design the Feature Association Decoupling Cross Stage Partial (FAD-CSP) module, which strengthens the association of long and short range features, improving the network performance in complex underwater environments. Additionally, our sophisticated post-processing method, based on non-maximum suppression with aspect-ratio similarity thresholds, optimizes detection in dense scenes, such as waterweed and schools of fish, improving object detection accuracy. Extensive experiments on the URPC and RUOD datasets demonstrate that our method outperforms existing state-of-the-art methods in terms of accuracy and noise immunity. AMSP-UOD proposes an innovative solution with the potential for real-world applications. Our code is available at https://github.com/zhoujingchun03/AMSP-UOD.

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EAAI Journal 2024 Journal Article

Contrastive decoupling global and local features for pavement crack detection

Ching-Chi Yeung
Kin-Man Lam

Pavement crack detection is an essential defect inspection task to ensure traffic and driving safety. Despite the recent studies that have achieved promising performance for inspecting pavement cracks, they still suffer from the challenges of background complexity, crack diversity, and generalization ability. To address these problems, we propose a contrastive decoupling network (CDNet) for detecting pavement cracks. Specifically, this contrastive decoupling framework separately extracts the global and local features with contrastive learning. It can effectively boost the discriminative power and generalization ability of the feature representations. Moreover, we propose a global semantic enhancement (GSE) module to enhance the semantic information of the global features. This module can reinforce the distinguishability for accurately identifying cracks and backgrounds. Furthermore, we propose a local detail refinement (LDR) module to refine the detailed information of the local features. This module can strengthen the localizability for detecting cracks with precise shapes. In addition, we propose a dynamic dependency-aware feature aggregation (DDFA) scheme to adaptively integrate the global and local features based on contextual dependencies. This scheme can enrich the output features for effectively detecting cracks in each image. Experimental results on four pavement crack detection datasets, namely Crack500, CrackTree200, CFD, and AEL, demonstrate that our proposed method outperforms the state-of-the-art methods.

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IS Journal 2023 Journal Article

Point Cloud Registration Using Multiattention Mechanism and Deep Hybrid Features

Yu-Xin Zhang
Zhan-Li Sun
Zhi-Gang Zeng
Kin-Man Lam

Due to some unfavorable factors, how to accurately register point clouds is still a challenging task. In this article, an effective point cloud registration network is proposed with multiple attention mechanism and deep hybrid features. For the features obtained with a graph neural network, three attention modules, namely the spatial attention module, channel attention module, and self-geometric attention module, are utilized to mine various areas of regional information. An attention-based feature fusion module, which consists of three consecutive residual blocks, is devised to fuse the features from the three attention modules. Moreover, the capability of the network for correctly matching point clouds is enhanced, by using deep hybrid features to guide the correspondence search and the calculation of matching confidence. Experimental results on several widely used datasets demonstrate the effectiveness of the proposed point cloud registration network.

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IJCAI Conference 2020 Conference Paper

Pay Attention to Devils: A Photometric Stereo Network for Better Details

Yakun Ju
Kin-Man Lam
Yang Chen
Lin Qi
Junyu Dong

We present an attention-weighted loss in a photometric stereo neural network to improve 3D surface recovery accuracy in complex-structured areas, such as edges and crinkles, where existing learning-based methods often failed. Instead of using a uniform penalty for all pixels, our method employs the attention-weighted loss learned in a self-supervise manner for each pixel, avoiding blurry reconstruction result in such difficult regions. The network first estimates a surface normal map and an adaptive attention map, and then the latter is used to calculate a pixel-wise attention-weighted loss that focuses on complex regions. In these regions, the attention-weighted loss applies higher weights of the detail-preserving gradient loss to produce clear surface reconstructions. Experiments on real datasets show that our approach significantly outperforms traditional photometric stereo algorithms and state-of-the-art learning-based methods.

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