Construction Vehicles Recognition Algorithm Based on Improved YOLOv7 in High Risk Areas

NAVIGATE

Table of Contents

STATISTICS

Viewed31

Downloads26

Construction Vehicles Recognition Algorithm Based on Improved YOLOv7 in High Risk Areas

[HTML] PDF下载 (26)

[1]ZHANG Zhen,XIAO Zongrong,LI Youhao,et al.Construction Vehicles Recognition Algorithm Based on Improved YOLOv7 in High Risk Areas[J].Journal of Zhengzhou University (Engineering Science),2025,46(05):1-8.[doi:10.13705/j.issn.1671-6833.2025.02.019]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 46 Number of periods: 2025 05 Page number: 1-8 Column: Public date: 2025-08-10

Title:: Construction Vehicles Recognition Algorithm Based on Improved YOLOv7 in High Risk Areas

Author(s):: ZHANG Zhen¹; XIAO Zongrong²; LI Youhao³; HUANG Weitao³; 1.School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 2.School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China; 3.Henan Huirong Oil and Gas Technology Co., Ltd., Zhengzhou 450001, China

Keywords:: highrisk areas; construction vehicles; YOLOv7; attention mechanism; upsampling; feature extraction

CLC:: TP391

DOI:: 10.13705/j.issn.1671-6833.2025.02.019

Abstract:: To address the safety risks posed by construction vehicles operations in highrisk areas near natural gas pipelines, particularly the physical impacts and environmental disturbances caused by heavy vehicles, in this study an improved YOLOv7-based construction vehicles recognition algorithm was proposed. Six common types of construction vehicles including dump trucks, rollers, mixers, forklifts, excavators, and loaders were selected as the research objects. A custom dataset, containing images captured in various environments and angles, was used to train the model, ensuring its performance. Firstly, the CBAM attention mechanism was introduced into the YOLOv7 head, and an improved GAM attention mechanism was added to the max pooling layer to enhance the model′s focus on key image features and improve detection accuracy. Secondly, the DySample dynamic upsampling module replaced the nearest neighbor interpolation, boosting precision. Finally, an improved SPPCSPC module was designed to enhance feature extraction efficiency, reduce computational costs, and accelerate inference. These modifications could enable the model to maintain high detection accuracy even in challenging scenarios such as low-quality images or distant targets. Experimental results demonstrated that the proposed algorithm achieved a precision P of 97.7%, recall R of 94.7%, mAP@0.5 of 98.6%, and mAP@0.5∶0.95 of 90.4%. Compared to the original YOLOv7 algorithm, these metrics improved by 1.3, 1.4, 1.4, and 3.7 percentage points, respectively.

References:: [1]胡皓,郭放,刘钊.改进YOLOX-S模型的施工场景目标检测[J]. 计算机科学与探索, 2023, 17(5): 1089-1101.
HU H, GUO F, LIU Z. Object detection based on improved YOLOX-S model in construction sites[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(5): 1089-1101.
[2]张震,李浩方,李孟洲,等.改进YOLOv3算法与人体信息数据融合的视频监控检测方法[J].郑州大学学报(工学版), 2021, 42(1):28-34.
ZHANG Z, LI H F, LI M Z, et al. Video surveillance detection method based on improved YOLOv3 algorithm and human body information data fusion[J]. Journal of Zhengzhou University(Engineering Science),2021,42 (1):28-34.
[3]李柯泉,陈燕,刘佳晨,等. 基于深度学习的目标检测算法综述[J]. 计算机工程,2022,48(7): 1-12.
LI K Q, CHEN Y, LI J C, et al. Survey of deep learning-based object detection algorithms[J]. Computer Engineering, 2022, 48(7): 1-12.
[4]GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2014: 580-587.
[5]REN S Q, HE K M, GIRSHICK R, et al. Faster RCNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[6]LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shotmultibox detector[C]∥Proceedings of the European Conference on Computer Vision. Berlin:Springer,2016: 21-37.
[7]LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Maching Intelligence, 2020,42 (2): 318-327.
[8]REDMON J,DIVVALA S,GIRSHICK R,et al. You only look once: unified,realtime object detection[C] ∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE,2016:779-788.
[9]张震,王晓杰,晋志化,等.基于轻量化YOLOv5的交通标志检测[J].郑州大学学报(工学版),2024,45(2): 12-19.
ZHANG Z, WANG X J, JIN Z H, et al. Traffic sign detection based on lightweight YOLOv5[J]. Journal of Zhengzhou University(Engineering Science), 2024,45 (2) :12-19.
[10]WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-theart for real-time object detectors[C]∥2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2023:7464-7475.
[11]李松江,吴宁,王鹏,等.基于改进 Cascade RCNN 的车辆目标检测方法[J].计算机工程与应用,2021,57 (5):123-130.
LI S J, WU N, WANG P, et al. Vehicle target detection method based on improved Cascade RCNN[J]. Computer Engineering and Applications, 2021,57(5):123-130.
[12]周康,朱宗晓,徐征宇,等.改进 Faster R-CNN的道路目标检测[J].计算机与数字工程,2022,50(4): 750-756.
ZHOU K, ZHU Z X, XU Z Y, et al. Road target detection based on improved Faster R-CNN[J]. Computer Engineering, 2022, 50(4):750-756.
[13]李国进,胡洁,艾矫燕.基于改进 SSD 算法的车辆检测[J].计算机工程,2022,48(1):266-274.
LI G J, HU J, AI J Y. Vehicle detection based on improved SSD algorithm[J]. Computer Engineering,2022, 48(1): 266-274.
[14]胡习之, 魏征, 周文超. 融合 Camshift 与 YOLOv4 车辆检测算法[J]. 机床与液压,2021,49(11):70-74.
HU X Z, WEI Z, ZHOU W C. Fusion of Camshift and YOLOv4 vehicle detection algorithm[J].Machine Tool & Hydraulics,2021,49(11):70-74.
[15] ZHANG Z X. Drone-YOLO: an efficient neural network method for target detection in drone images[J]. Drones, 2023, 7(8): 526.
[16] HE K, ZHANG X Y, REN S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.
[17]WANG C Y, LIAO H Y M, WU Y H, et al. CSPNet: a new backbone that can enhance learning capability of CNN[C]∥2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piacataway: IEEE,2020:1571-1580.
[18]唐靓, 余明慧, 武明虎,等. 基于改进 YOLOv5的绝缘子缺陷检测算法[J].华中师范大学学报(自然科学版), 2022, 56(5): 771-780.
TANG J, YU M H, WU M H, et al. Insulator defect detection algorithm based on improved YOLOv5[J]. Journal of Central China Normal University (Natural Sciences), 2022, 56(5): 771-780.
[19] LIU Y C, SHAO Z R, NICO H. Global attention mechanism: retain information to enhance channel-spatial interactions[EB/OL].(2021-12-10)[2024-08-16].https:∥ doi.org/10.48550/arXiv.2112.05561.
[20]WOO S, PARK J, LEE J, et al. CBAM: convolutional block attention module[EB/OL]. (2018-07-17)[202408-16].https:∥doi.org/10.48550/arXiv.1807.06521.
[21] LIU W Z, LU H, FU H T, et al. Learning to upsample by learning to sample[EB/OL]. (2023-08-29)[202408-16].https:∥doi.org/10.48550/arXiv.2308.15085.
[22] LI Y J, MA C Y, LI L P, et al. Lightweight tunnel obstacle detection based on improved YOLOv5[J]. Sensors, 2024, 24(2):395.
[23]张徐,朱正为,郭玉英,等. 基于cosSTR-YOLOv7的多尺度遥感小目标检测[J].电光与控制,2024,31(4): 28-34.
ZHANG X, ZHU Z W, GUO Y Y, et al. Multi-scale remote sensing small target detection based on cosSTRYOLOv7[J]. Electronics Optics & Control, 2024, 31 (4):28-34.
[24] FAHAD J, JANIBUL A S, ABU N M,et al. Detection of mold on the food surface using YOLOv5[J]. Current Research in Food Science,2021,4: 724-728.
[25] SHE F F, HONG Z Y, ZENG Z Q,et al. Improved traffic sign detection model based on YOLOv7-tiny[J]. IEEE Access,2023, 11: 126555-126567.
[26]WANG F, WANG H Y, QIN Z Y ,et al. UAV target detection algorithm based on improved YOLOv8[J]. IEEE Access,2023,11: 116534-116544.

Similar References:

Memo

Last Update: 2025-09-19