Enhanced YOLOv5: An Efficient Road Object Detection Method
Accurate identification of road objects is crucial for achieving intelligent traffic systems. However, developing efficient and accurate road object detection methods in complex traffic scenarios has always been a challenging task. The objective of this study was to improve the target detection algo...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/20/8355 |
| _version_ | 1797572327894941696 |
|---|---|
| author | Hao Chen Zhan Chen Hang Yu |
| author_facet | Hao Chen Zhan Chen Hang Yu |
| author_sort | Hao Chen |
| collection | DOAJ |
| description | Accurate identification of road objects is crucial for achieving intelligent traffic systems. However, developing efficient and accurate road object detection methods in complex traffic scenarios has always been a challenging task. The objective of this study was to improve the target detection algorithm for road object detection by enhancing the algorithm’s capability to fuse features of different scales and levels, thereby improving the accurate identification of objects in complex road scenes. We propose an improved method called the Enhanced YOLOv5 algorithm for road object detection. By introducing the Bidirectional Feature Pyramid Network (BiFPN) into the YOLOv5 algorithm, we address the challenges of multi-scale and multi-level feature fusion and enhance the detection capability for objects of different sizes. Additionally, we integrate the Convolutional Block Attention Module (CBAM) into the existing YOLOv5 model to enhance its feature representation capability. Furthermore, we employ a new non-maximum suppression technique called Distance Intersection Over Union (DIOU) to effectively address issues such as misjudgment and duplicate detection when significant overlap occurs between bounding boxes. We use mean Average Precision (mAP) and Precision (P) as evaluation metrics. Finally, experimental results on the BDD100K dataset demonstrate that the improved YOLOv5 algorithm achieves a 1.6% increase in object detection mAP, while the P value increases by 5.3%, effectively improving the accuracy and robustness of road object recognition. |
| first_indexed | 2024-03-10T20:54:41Z |
| format | Article |
| id | doaj.art-6a508b35cc9b4c37983b5f2ae68dc162 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:54:41Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6a508b35cc9b4c37983b5f2ae68dc1622023-11-19T18:01:36ZengMDPI AGSensors1424-82202023-10-012320835510.3390/s23208355Enhanced YOLOv5: An Efficient Road Object Detection MethodHao Chen0Zhan Chen1Hang Yu2School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, ChinaSchool of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, ChinaSchool of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, ChinaAccurate identification of road objects is crucial for achieving intelligent traffic systems. However, developing efficient and accurate road object detection methods in complex traffic scenarios has always been a challenging task. The objective of this study was to improve the target detection algorithm for road object detection by enhancing the algorithm’s capability to fuse features of different scales and levels, thereby improving the accurate identification of objects in complex road scenes. We propose an improved method called the Enhanced YOLOv5 algorithm for road object detection. By introducing the Bidirectional Feature Pyramid Network (BiFPN) into the YOLOv5 algorithm, we address the challenges of multi-scale and multi-level feature fusion and enhance the detection capability for objects of different sizes. Additionally, we integrate the Convolutional Block Attention Module (CBAM) into the existing YOLOv5 model to enhance its feature representation capability. Furthermore, we employ a new non-maximum suppression technique called Distance Intersection Over Union (DIOU) to effectively address issues such as misjudgment and duplicate detection when significant overlap occurs between bounding boxes. We use mean Average Precision (mAP) and Precision (P) as evaluation metrics. Finally, experimental results on the BDD100K dataset demonstrate that the improved YOLOv5 algorithm achieves a 1.6% increase in object detection mAP, while the P value increases by 5.3%, effectively improving the accuracy and robustness of road object recognition.https://www.mdpi.com/1424-8220/23/20/8355intelligent trafficenhanced YOLOv5multi-scaleroad object detection |
| spellingShingle | Hao Chen Zhan Chen Hang Yu Enhanced YOLOv5: An Efficient Road Object Detection Method Sensors intelligent traffic enhanced YOLOv5 multi-scale road object detection |
| title | Enhanced YOLOv5: An Efficient Road Object Detection Method |
| title_full | Enhanced YOLOv5: An Efficient Road Object Detection Method |
| title_fullStr | Enhanced YOLOv5: An Efficient Road Object Detection Method |
| title_full_unstemmed | Enhanced YOLOv5: An Efficient Road Object Detection Method |
| title_short | Enhanced YOLOv5: An Efficient Road Object Detection Method |
| title_sort | enhanced yolov5 an efficient road object detection method |
| topic | intelligent traffic enhanced YOLOv5 multi-scale road object detection |
| url | https://www.mdpi.com/1424-8220/23/20/8355 |
| work_keys_str_mv | AT haochen enhancedyolov5anefficientroadobjectdetectionmethod AT zhanchen enhancedyolov5anefficientroadobjectdetectionmethod AT hangyu enhancedyolov5anefficientroadobjectdetectionmethod |