Design of an Efficient Object Detection Algorithm for Traffic Accident Recognition

Technical Summary: Design of an Efficient Object Detection Algorithm for Traffic Accident Recognition

Problem Statement
The increasing density of urban traffic and the complexity of road environments have exposed significant limitations in existing traffic accident detection methods. Traditional manual reporting is often delayed, inaccurate, or missed entirely, particularly in scenarios involving unconscious victims, nighttime conditions, or remote locations, thereby missing the critical "golden rescue time." While deep learning models like YOLO11n offer strong detection capabilities, they struggle in complex traffic scenarios characterized by occlusion, dense vehicle flow, and adverse weather. Furthermore, the original YOLO11n architecture suffers from a large parameter count and high computational cost, making efficient real-time deployment on edge devices (such as in-vehicle terminals and roadside units) difficult. Existing alternative methods often fail to balance accuracy with lightweight efficiency or lack adaptability to complex backgrounds.

Methodology
To address these challenges, the authors propose SDD-YOLO11n, a lightweight improved object detection algorithm based on the YOLO11n architecture. The methodology focuses on three primary structural modifications to enhance inference speed, reduce computational load, and improve feature representation in challenging conditions:

1. ShuffleNetV2 Backbone: The original backbone is replaced with ShuffleNetV2. This module utilizes pointwise grouped convolutions and channel shuffle mechanisms. By dividing input feature maps into subgroups and convolving them separately, the model significantly reduces the parameter count and computational complexity (to 1/n of the original). The channel shuffle operation facilitates cross-group information exchange, preventing information blockage and maintaining high detection accuracy while ensuring low computational complexity suitable for edge devices.
2. DW_ADown Downsampling Module: To mitigate information loss and computational redundancy in traditional downsampling layers, the authors introduce the DW_ADown module. This module integrates the concepts of ADown and Depthwise Convolution (DWConv). It replaces standard convolutions with depthwise separable convolutions (consisting of depthwise and pointwise convolutions) and incorporates an ADown structure that splits feature maps, processes them via average pooling, 3×3 strided convolution, and max pooling, and then concatenates the branches. This design preserves spatial feature extraction capabilities while minimizing information loss and reducing the computational burden.
3. DS_SENetV2 Feature Enhancement: The original SPPF (Spatial Pyramid Pooling Fast) module is replaced with DS_SENetV2. This module combines the multi-scale feature aggregation of SPPF with depthwise separable convolutions and an improved SENetV2 architecture. It employs a Squeeze-and-Excitation (SE) mechanism to adaptively recalibrate channel-wise feature responses. By modeling inter-channel dependencies, the module enhances the network's ability to capture global contextual knowledge and channel patterns, improving feature representation and robustness against background noise and occlusion.

Key Contributions

• Lightweight Architecture: The integration of ShuffleNetV2, DW_ADown, and DS_SENetV2 creates a model that significantly reduces computational complexity and parameter size without sacrificing detection performance.
• Enhanced Generalization: The proposed modules specifically target the reduction of information loss during downsampling and the improvement of feature representation in occluded and complex weather conditions.
• Edge-Ready Deployment: The algorithm is explicitly designed to meet the dual demands of real-time performance and high accuracy on resource-constrained edge devices.

Experimental Results
The model was evaluated on a processed VADV dataset containing 9,787 images of traffic accidents and standard vehicles, augmented with scenarios involving rain, poor lighting, and dense occlusion. The results demonstrate the following improvements over the baseline YOLO11n:

• Accuracy: The mean Average Precision at IoU 0.5 (mAP@0.5) improved by 1.3% (from 0.811 to 0.824).
• Efficiency: The number of parameters was reduced by 69.1% (from 2.59M to 0.80M), and FLOPs decreased by 65.6% (from 6.4G to 2.2G).
• Speed: The model maintained a frame rate of 70.9 FPS, which, while slightly lower than the original YOLO11n (75 FPS), remains well within real-time detection requirements.
• Comparative Performance: Compared to mainstream models like YOLOv8, YOLOv9, and SSD, SDD-YOLO11n achieved superior or comparable accuracy with significantly fewer parameters and lower computational costs. For instance, compared to SSD, it improved mAP@0.5 by 1.0% and increased inference speed by approximately 2.7 times. Visual comparisons confirmed that SDD-YOLO11n achieved higher confidence scores and fewer missed detections or false positives than competing models in test scenarios.

Significance and Limitations
The paper claims that SDD-YOLO11n offers an excellent balance between detection accuracy and computational efficiency, making it highly suitable for real-time traffic accident detection in complex environments where edge deployment is necessary. The model demonstrates superior stability during training and strong generalization capabilities.

However, the authors modestly acknowledge limitations: the current dataset does not include a detailed categorization of specific traffic accident types. Future work is planned to focus on deploying the network on actual embedded devices and refining the dataset with more granular accident classifications. The study emphasizes that while the model is highly efficient, its application is currently bounded by the scope of the available annotated data.