基于改进的YOLOX_S的车辆检测研究
Zhihai Liu1, Wenyu Han1, Hao Xu1
1College of Transportation, Shandong University of Science and Technology, Qingdao, 266590, China.
Scientific reports
|December 28, 2023
概括
这项研究通过改进YOLOX_S模型来更好地识别远处的小型车辆,从而提高了交通中的车辆检测. 改进的模型减少了错过的检测和堵塞,这对于智能交通系统至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 在交通场景中检测车辆的挑战是小的,遥远的目标和阻塞.
- 像YOLOX_S这样的现有模型可以错误检测或省略这些关键目标.
研究的目的:
- 提出一个改进的YOLOX_S模型,以加强对小型远程车辆的检测.
- 为应对对挑战性交通场景的精度和特征表示的局限性.
主要方法:
- 模型压缩以优化YOLOX_S推断速度.
- 在剩余结构 (Resunit_CA) 中集成一个坐标注意力模块,以增强小目标特征的注意力.
- 在PAFPN结构中添加一个自适应特征融合模块,以获得更丰富的特征提取.
- 用焦点损失优化脱头来处理样本失衡.
主要成果:
- 改进的模型在实验数据集上实现了77.19%的平均检测准确度.
- 有效地缓解了小目标错误检测和多目标封闭的问题.
- 检测速度降至29.73fps,表明实时优化的空间.
结论:
- 拟议的YOLOX_S增强有效地改善了在交通中检测小,遥远的车辆.
- 这些修改成功地减少了错过的检测,并改善了封闭目标的处理.
- 需要进行进一步的研究,以平衡精度增长与实时检测速度要求.
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