YOLO-SCDI: A Vehicle Detection Algorithm Based on an Improved YOLOv8

WU Lin; CAO Wen

doi:10.11999/JEIT250238

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WU Lin, CAO Wen. YOLO-SCDI: A Vehicle Detection Algorithm Based on an Improved YOLOv8[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250238

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WU Lin, CAO Wen. YOLO-SCDI: A Vehicle Detection Algorithm Based on an Improved YOLOv8[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250238

WU Lin, CAO Wen. YOLO-SCDI: A Vehicle Detection Algorithm Based on an Improved YOLOv8[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250238

Citation:

WU Lin, CAO Wen. YOLO-SCDI: A Vehicle Detection Algorithm Based on an Improved YOLOv8[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250238

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YOLO-SCDI: A Vehicle Detection Algorithm Based on an Improved YOLOv8

doi: 10.11999/JEIT250238 cstr: 32379.14.JEIT250238

WU Lin,
CAO Wen^,

School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China

Funds: Science and Technology Plan Project of Xi'an Science and Technology Bureau (24GXFW0097)

Received Date: 2025-04-07
Rev Recd Date: 2025-09-03

Available Online: 2025-09-09

Abstract

Abstract

Objective As a core task in computer vision, object detection is vital for intelligent transportation, supporting applications such as autonomous driving, Electronic Toll Collection (ETC), and traffic violation monitoring. However, complex urban environments—characterized by extreme weather, dense traffic occlusions, intense illumination, and reflective surfaces—pose substantial challenges, leading traditional methods to high false detection and missed detection rates. Despite recent progress, accuracy issues remain unresolved. To address these limitations, this study proposes YOLO-SCDI, a lightweight and effective vehicle detection model systematically optimized from YOLOv8 across four components: backbone network, neck structure, detection head, and loss function. These improvements significantly enhance detection accuracy and robustness in complex traffic conditions while maintaining model compactness and inference efficiency. Methods Building on the YOLOv8n architecture, four top-down optimization strategies are designed to balance detection accuracy, parameter efficiency, and lightweight deployment. First, to address the limited feature representation capacity, an attention-enhanced C2f-SCSA module (Fig. 4) is proposed. This module dynamically integrates local and global features through multi-scale convolutions and a dual spatial–channel attention mechanism, thereby improving the quality of input features. Second, to achieve effective multi-scale information integration while preserving both detailed and contextual features, a lightweight Cross-scale Feature Fusion Module (CCFM) is introduced into the Neck structure (Fig. 5). This results in the CCFM-Neck architecture, which reduces parameter size and enhances sensitivity to small-scale targets. Third, to mitigate the limitations of YOLOv8’s detection head—such as fixed feature fusion patterns and weak dynamic cross-scale interactions—a Dynamic Head module is incorporated. This module jointly models scale, spatial, and task attention, and includes a dynamic convolution-kernel generation network that adjusts convolution weights in real time according to input features. These improvements strengthen classification and regression feature responses, increasing the adaptability and discriminability of the detection head. Finally, because the CIoU loss function shows insufficient localization accuracy for small or irregular targets, ShapeIoU is adopted as the loss function. It is further improved using the Inner-IoU concept, which accelerates model convergence and enhances localization performance. Results and Discussions YOLO-SCDI is evaluated against mainstream detection models on the UA-DETRAC and BDD100K datasets. On the UA-DETRAC dataset (Table 4), YOLO-SCDI achieves an optimal balance between resource efficiency and detection performance. It requires only 2.37 M parameters and 7.6 GFLOPs—substantially fewer than competing models—while attaining 95.8% mAP@0.5, a 2.5% improvement over the baseline YOLOv8n and higher than most mainstream detectors. Under the stricter mAP@0.5:0.95 metric, YOLO-SCDI reaches 80.3%, clearly outperforming other lightweight designs. On the BDD100K dataset (Table 5), YOLO-SCDI improves mAP@0.5 and mAP@0.5:0.95 by 1.4% and 1.1%, respectively, compared with the baseline. These results are consistent with those from the UA-DETRAC dataset, confirming strong generalization and robustness. Detection results under varying illumination (Fig. 7) and adverse weather (Fig. 8) further validate its performance in realistic complex scenarios. Compared with models such as NanoDet, YOLOv12n, and YOLOv8n, YOLO-SCDI effectively reduces missed and false detections while providing higher-confidence predictions and more precise localization. Additionally, ablation studies (Table 3) confirm the contributions of the proposed C2f-SCSA, Dynamic Head, and Inner-ShapeIoU modules to performance gains. Collectively, these results demonstrate that YOLO-SCDI markedly enhances detection accuracy while maintaining a lightweight structure, thereby meeting practical requirements for vehicle detection in complex traffic environments. Conclusions This study proposes YOLO-SCDI, a vehicle detection algorithm built on an improved YOLOv8 framework. By optimizing the backbone network, neck structure, detection head, and loss function, the method enhances detection accuracy while substantially reducing model parameters. Experimental evaluations demonstrate that YOLO-SCDI exceeds existing approaches in both accuracy and model efficiency, making it well suited for practical vehicle detection tasks in complex traffic environments.
- Vehicle Detection,
- SCSA,
- CCFM,
- Dynamic Head

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References(23)

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