YOMANet-Accel: A Lightweight Algorithm Accelerator for Pedestrians and Vehicles Detection at the Edge

CHEN Ningjiang; LU Yaozong

doi:10.11999/JEIT250059

Volume 47 Issue 8

Aug. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(8): 2895-2908

CHEN Ningjiang, LU Yaozong. YOMANet-Accel: A Lightweight Algorithm Accelerator for Pedestrians and Vehicles Detection at the Edge[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2895-2908. doi: 10.11999/JEIT250059

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CHEN Ningjiang, LU Yaozong. YOMANet-Accel: A Lightweight Algorithm Accelerator for Pedestrians and Vehicles Detection at the Edge[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2895-2908. doi: 10.11999/JEIT250059

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YOMANet-Accel: A Lightweight Algorithm Accelerator for Pedestrians and Vehicles Detection at the Edge

doi: 10.11999/JEIT250059 cstr: 32379.14.JEIT250059

CHEN Ningjiang^{1, 2, 3},
LU Yaozong^{1
,
,}

1.
School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
2.
Guangxi Center of Technology Innovation for Intelligent Digital Services, Nanning 530004, China
3.
Key Laboratory of Parallel, Distributed and Intelligent Computing (Guangxi University), Education Department of Guangxi Zhuang Autonomous Region, Nanning 530004, China

Funds: The National Natural Science Foundation of China (62162003), The Central Guidance on Local Science and Technology Development Fund of Guangxi Province (GuikeZY24212059)

Received Date: 2025-01-22
Rev Recd Date: 2025-06-30

Available Online: 2025-07-04

Publish Date: 2025-08-27

Abstract

Abstract

Objective Accurate and real-time detection of pedestrians and vehicles is essential for autonomous driving at the edge. However, deep learning-based object detection algorithms are often challenging to deploy in edge environments due to their high computational demands and complex parameter structures. To address these limitations, this study proposes a soft-hard coordination strategy. A lightweight neural network model, Yolo Model Adaptation Network (YOMANet), is designed, and a corresponding neural network accelerator, YOMANet Accelerator (YOMANet-Accel), is implemented on a heterogeneous Field-Programmable Gate Array (FPGA) platform. This system enables efficient algorithm acceleration for pedestrian and vehicle detection in edge-based autonomous driving scenarios. Methods The lightweight backbone of YOMANet adopts MobileNetv2 to reduce the number of network parameters. The neck network incorporates the Spatial Pyramid Pooling (SPP) and Path Aggregation Network (PANet) structures from YOLOv4 to expand the receptive field and accommodate targets of varying sizes. Depthwise separable convolution replaces standard convolution, thereby reducing training complexity and improving convergence speed. To enhance detail extraction, the Normalization-based Attention Module (NAM) is integrated into the head network, allowing suppression of irrelevant feature weights. For deployment on a FPGA platform, parallel computing and data storage schemes are designed. The parallel computing strategy adopts a loop blocking method to reorder inner and outer loops, enabling access to different output array elements through adjacent loop layers and facilitating parallel processing of output feature map pixels. Multiply-add trees are implemented in the Processing Engine (PE) to support efficient task allocation and operation scheduling. A double-buffer mechanism is introduced in the data storage scheme to increase data reuse, minimize transmission latency, and enhance system throughput. In addition, int8 quantization is applied to both weight parameters and activation functions, reducing the overall parameter size and accelerating parallel computation. Results and Discussions Experimental results on the training platform indicate that YOMANet achieves the inference speed characteristic of lightweight models while maintaining the detection accuracy of large-scale models, thereby improving overall detection performance (Fig. 12, Table 2). The ablation study demonstrates that the integration of MobileNetv2 and depthwise separable convolution significantly reduces the number of model parameters. Embedding the NAM attention mechanism does not noticeably increase model size but enhances detail extraction and improves detection of small targets (Table 3). Compared with other lightweight algorithms, the enhanced YOMANet shows improved detail extraction and superior detection of small and occluded targets, with substantially lower false and missed detection rates (Fig. 13). Results on the accelerator platform reveal that quantization has minimal effect on accuracy while substantially reducing model size, supporting deployment on resource-constrained edge devices (Table 4). When deployed on the FPGA platform, YOMANet retains detection accuracy comparable to GPU/CPU platforms, while power consumption is reduced by an order of magnitude, meeting the efficiency requirements for edge deployment (Fig. 14). Compared with related accelerator designs, YOMANet-Accel achieves competitive throughput and the highest Digital Signal Processing (DSP) efficiency, demonstrating the effectiveness of the proposed parallel computing and storage schemes in utilizing FPGA resources (Table 5). Conclusions Experimental results demonstrate that YOMANet achieves high detection accuracy and fast inference speed on the training platform, with enhanced performance for small and occluded targets, leading to a reduced missed detection rate. When deployed on the FPGA platform, YOMANet-Accel achieves an effective balance between detection performance and resource efficiency, supporting real-time pedestrian and vehicle detection in edge computing scenarios.
- Pedestrians and vehicles detection,
- Edge computing,
- Lightweight model,
- Heterogeneous FPGA acceleration

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References(29)

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