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Yi Kang

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3 papers

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EAAI Journal 2025 Journal Article

Fast shallow multi-subnet detector for real-time object detection

Yuan Li
Mengdie Song
Ke Hu
Song Chen
Yi Kang

Real-time object detection algorithms, underpinned by Deep Neural Networks (DNNs), are extensively applied in fields like autonomous driving and security surveillance. However, current algorithms face issues of low hardware resource utilization and high synchronization delays between network layers when deployed on DNN hardware accelerators, adversely affecting overall performance and efficiency. To address these issues, we have proposed an innovative single-stage object detection framework, the Shallow Multi-Subnet Detector (SMS-Det). SMS-Det adopts a multi-parallel-shallow-subnet architecture, which reduces inter-layer synchronization latency by decreasing network depth. Furthermore, it fully utilizes DNN hardware accelerators by executing convolution operations in parallel, preventing resource underutilization and maximizing throughput. The proposed network is comprised of multiple parallel shallow subnets, each of which processes feature maps of different scales. The Feature Fusion Layer (FFL) ensures seamless information exchange across subnets, significantly improving the detection of small and occluded objects. Finally, we introduce the multi-scale channel attention projections to enhance the feature mapping between the teacher model and the student model in the training process. Experimental results on the Microsoft Common Objects in Context (MS COCO) dataset demonstrate that our model achieves a state-of-the-art mean Average Precision (mAP) of 42. 6%, surpassing You Only Look Once Version 5 Small (YOLOv5-S 37. 4%) with only 19. 4 Giga Floating Point Operations (GFLOPs) and 11. 0 million parameters. Our model obtains 156 Frames Per Second (FPS), achieving a real-time inference acceleration of 51. 4% compared to YOLOv5-S (103 FPS).

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AAAI Conference 2024 Conference Paper

DALDet: Depth-Aware Learning Based Object Detection for Autonomous Driving

Ke Hu
Tongbo Cao
Yuan Li
Song Chen
Yi Kang

3D object detection achieves good detection performance in autonomous driving. However, it requires substantial computational resources, which prevents its practical application. 2D object detection has less computational burden but lacks spatial and geometric information embedded in depth. Therefore, we present DALDet, an efficient depth-aware learning based 2D detector, achieving high-performance object detection for autonomous driving. We design an efficient one-stage detection framework and seamlessly integrate depth cues into convolutional neural network by introducing depth-aware convolution and depth-aware average pooling, which effectively improve the detector's ability to perceive 3D space. Moreover, we propose a depth-guided loss function for training DALDet, which effectively improves the localization ability of the detector. Due to the use of depth map, DALDet can also output the distance of the object, which is of great importance for driving applications such as obstacle avoidance. Extensive experiments demonstrate the superiority and efficiency of DALDet. In particular, our DALDet ranks 1st on both KITTI Car and Cyclist 2D detection test leaderboards among all 2D detectors with high efficiency as well as yielding competitive performance among many leading 3D detectors. Code will be available at https://github.com/hukefy/DALDet.

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JBHI Journal 2021 Journal Article

Rapidly Decoding Image Categories From MEG Data Using a Multivariate Short-Time FC Pattern Analysis Approach

Chunyu Liu
Yi Kang
Lingxi Zhang
Jiacai Zhang

Recent advances in the development of multivariate analysis methods have led to the application of multivariate pattern analysis (MVPA) to investigate the interactions between brain regions using graph theory (functional connectivity, FC) and decode visual categories from functional magnetic resonance imaging (fMRI) data from a continuous multicategory paradigm. To estimate stable FC patterns from fMRI data, previous studies required long periods in the order of several minutes, in comparison to the human brain that categories visual stimuli within hundreds of milliseconds. Constructing short-time dynamic FC patterns in the order of milliseconds and decoding visual categories is a relatively novel concept. In this study, we developed a multivariate decoding algorithm based on FC patterns and applied it to magnetoencephalography (MEG) data. MEG data were recorded from participants presented with image stimuli in four categories (faces, scenes, animals and tools). MEG data from 17 participants demonstrate that short-time dynamic FC patterns yield brain activity patterns that can be used to decode visual categories with high accuracy. Our results show that FC patterns change over the time window, and FC patterns extracted in the time window of 0∼200 ms after the stimulus onset were most stable. Further, the categorizing accuracy peaked (the mean binary accuracy is above 78. 6% at individual level) in the FC patterns estimated within the 0∼200 ms interval. These findings elucidate the underlying connectivity information during visual category processing on a relatively smaller time scale and demonstrate that the contribution of FC patterns to categorization fluctuates over time.

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