Lightweight Real-Time Object Detection via Enhanced Global Perception and Intra-Layer Interaction for Complex Traffic Scenarios

Authors

  • Ben Liang Hebei University of Science and Technology https://orcid.org/0000-0002-9619-6380
  • Jia Su Hebei University of Science and Technology
  • Kangkang Feng Hebei University of Science and Technology https://orcid.org/0009-0002-9541-4371
  • Yongqiang Zhang Hebei University of Science and Technology
  • Weimin Hou Hebei University of Science and Technology

Keywords:

object detection, complex autonomous driving, real-time, global dependencies, multi-branch scale-aware

Abstract

Due to unfavorable factors such as cluttered spatial and temporal distribution of multiple types of targets, occlusion of background objects of different shapes, and blurring of feature information by inclement weather, the low detection accuracy in complex traffic scenarios has been a troubling issue. Regarding the above-mentioned issues, the paper proposes a lightweight real-time detection network to augment multi-scale object perception capabilities in traffic scenarios while ensuring real-time detection speed. First, we construct a novel global feature extraction (GFE) structure by cascading orthogonal band convolution kernels that capture the global dependencies between pixels to improve feature discrimination. Then, an intra-layer multi-scale feature interaction (IMFI) module is proposed to reinforce the effective reuse and multi-level transfer of salient features. In addition, we build a multi-branch scale-aware aggregation (MSA) module that captures abundant context-associated features to improve the target decision-making capability and the self-adaptive capability of the model when dealing with diverse object scales. Experimental results demonstrate that the proposed approach attains a significant improvement of 5.6 percentage points in AP50 with fewer parameters and computational power compared to the baseline model, with an improved FPS of 73. Furthermore, our approach strikes the optimal speed-accuracy balance when compared against other excellent object detection algorithms of the same magnitude.

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Author Biographies

Ben Liang, Hebei University of Science and Technology

Ben Liang received the B.S. degree in communication engineering from West Anhui University, Anhui, China. He is currently pursuing the M.E. degree with the Hebei University of Science and Technology. His current research interests include computer vision, image processing, and object detection.

Jia Su, Hebei University of Science and Technology

Jia Su received the Ph.D. degree in communication and information systems from Harbin Engineering University, Heilongjiang, China, in 2010. She is currently a Professor with the School of Information Science and Engineering, Hebei University of Science and Technology. Her research interests include multi-antenna array, image processing, computer vision, and object detection.

Kangkang Feng, Hebei University of Science and Technology

Kangkang Feng received the B.S. degree in communication engineering from the Polytechnic College of Hebei University of Science and Technology, Shijiazhuang, China. He is currently pursuing the M.E. degree with the Hebei University of Science and Technology. His current research interests include image processing and object detection.

Yongqiang Zhang, Hebei University of Science and Technology

Yongqiang Zhang received the M.E. degree in Anhui University of Technology, majoring in computer application technology. He is currently pursuing a doctor's degree in Army Engineering University of PLA. He is a director of the Hebei Industrial Internet Industry Alliance and deputy director of the Hebei Intelligent Internet of Things Technology Innovation Center. His research interests include artificial intelligence technology and Internet of things technology.

Weimin Hou, Hebei University of Science and Technology

Weimin Hou received the Ph.D. degree in signal and information processing from the Institute of Acoustics, Chinese Academy of Sciences, Beijing, China, in 2007. He is currently a Professor with the School of Information Science and Engineering, Hebei University of Science and Technology. His research interests include array signal processing, wireless communication, remote sensing image processing, and artificial intelligence.

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Published

2024-03-13

How to Cite

Liang, B., Su, J., Feng, K., Zhang, Y., & Hou, W. (2024). Lightweight Real-Time Object Detection via Enhanced Global Perception and Intra-Layer Interaction for Complex Traffic Scenarios. IEEE Latin America Transactions, 22(4), 312–320. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/8420

Issue

Vol. 22 No. 4 (2024): Ordinary Issue

Section

Computing