Improved YOLOv7 based facial detection of tractor drivers in complex environments

XU Hongmei; LI Yalin; LI Zhongxin; MENG Junshi; YANG Kangxin; LI Xurong

doi:10.13300/j.cnki.hnlkxb.2025.04.028

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Improved YOLOv7 based facial detection of tractor drivers in complex environments

Food science and agricultural engineering | 更新时间：2026-11-05

- Improved YOLOv7 based facial detection of tractor drivers in complex environments
- Journal of Huazhong Agricultural University Vol. 44, Issue 4, Pages: 288-301(2025)
- 作者机构：
  
  华中农业大学工学院/农业农村部长江中下游农业装备重点实验室，武汉 430070
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2025.04.028
  CLC： TP391.4
- Received：22 November 2024，
  
  Published：05 July 2025
- 稿件说明：
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徐红梅，李亚林，李中鑫，等.基于改进YOLOv7的复杂环境下拖拉机驾驶员面部检测［J］.华中农业大学学报，2025，44（4）：288‐301.

XU Hongmei,LI Yalin,LI Zhongxin,et al.Improved YOLOv7 based facial detection of tractor drivers in complex environments[J].Journal of Huazhong Agricultural University,2025,44(04):288-301.
徐红梅，李亚林，李中鑫，等.基于改进YOLOv7的复杂环境下拖拉机驾驶员面部检测［J］.华中农业大学学报，2025，44（4）：288‐301. DOI： 10.13300/j.cnki.hnlkxb.2025.04.028.

XU Hongmei,LI Yalin,LI Zhongxin,et al.Improved YOLOv7 based facial detection of tractor drivers in complex environments[J].Journal of Huazhong Agricultural University,2025,44(04):288-301. DOI： 10.13300/j.cnki.hnlkxb.2025.04.028.

摘要

为提高复杂场景下拖拉机驾驶员面部小目标检测精度，基于YOLOv7算法提出了一种精度高、泛化性强的驾驶员面部小目标检测方法。首先，构建改进空间金字塔池化模块，有效聚合低频全局信息与高频局部信息；其次，采用跨级部分网络模块，提高算法的计算效益；最后，调整检测层结构，构建全新检测头SC_C_detect，提高小目标特征提取能力。消融实验及对比试验结果显示，改进后的算法单张图片检测时间为7.8 ms，mAP@0.5为97.29%，mAP0.5：0.95为69.45%，优于Faster-RCNN、YOLOv5l、YOLOv8l等目标检测算法。在拖拉机不同振动水平下开展泛化性试验发现，改进后面部小目标检测模型的背景误差与定位误差均有所降低。结果表明，该算法兼具实时性与准确性，且在不同振动水平下，拥有良好的泛化性能。

Abstract

A high-precision and highly generalized method of detecting facial small object of driver based on YOLOv7 algorithm was proposed to address the issues of falsely detecting facial small target and the low accuracy of detection caused by vibration and background occlusion for tractor drivers in complex environments of agriculture.An improved spatial pyramid pooling module AS_SPPFCSPC was used to replace SPPCSPC to effectively aggregate low-frequency global information and high-frequency local information to enhance the accuracy of facial localization for drivers.The cross-level partial network module VoVGSDCSP was used to replace the E-ELAN module in the neck network to achieve higher computational efficiency of the algorithm.The structure of detection layer was adjusted and a new detection head SC_C_detect was introduced to improve the ability to extract small target features.The results of ablation and comparative experiments showed that the improved algorithm had a single-image detection time of 7.8 ms， with mAP@0.5 at 97.29% and mAP@0.5：0.95 at 69.45%， superior to object detection algorithms including Faster RCNN， YOLOv5l， and YOLOv8l.The results of generalization experiments conducted on tractors at levels of different vibration showed that the background error and localization error of the facial small target detection model after improvement were effectively reduced.It is indicated that the algorithm proposed combines real-time and accuracy， with good generalization performance at different levels of vibration.

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references

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