Development of Artificial Intelligent-Based Methodology to Prepare Input for Estimating Vehicle Emissions
Machine learning has significantly advanced traffic surveillance and management, with YOLO (You Only Look Once) being a prominent Convolutional Neural Network (CNN) algorithm for vehicle detection. This study utilizes YOLO version 7 (YOLOv7) combined with the Kalman-based SORT (Simple Online and Rea...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/23/11175 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846124471979606016 |
|---|---|
| author | Elif Yavuz Alihan Öztürk Nedime Gaye Nur Balkanlı Şeref Naci Engin S. Levent Kuzu |
| author_facet | Elif Yavuz Alihan Öztürk Nedime Gaye Nur Balkanlı Şeref Naci Engin S. Levent Kuzu |
| author_sort | Elif Yavuz |
| collection | DOAJ |
| description | Machine learning has significantly advanced traffic surveillance and management, with YOLO (You Only Look Once) being a prominent Convolutional Neural Network (CNN) algorithm for vehicle detection. This study utilizes YOLO version 7 (YOLOv7) combined with the Kalman-based SORT (Simple Online and Real-time Tracking) algorithm as one of the models used in our experiments for real-time vehicle identification. We developed the “ISTraffic” dataset. We have also included an overview of existing datasets in the domain of vehicle detection, highlighting their shortcomings: existing vehicle detection datasets often have incomplete annotations and limited diversity, but our “ISTraffic” dataset addresses these issues with detailed and extensive annotations for higher accuracy and robustness. The ISTraffic dataset is meticulously annotated, ensuring high-quality labels for every visible object, including those that are truncated, obscured, or extremely small. With 36,841 annotated examples and an average of 32.7 annotations per image, it offers extensive coverage and dense annotations, making it highly valuable for various object detection and tracking applications. The detailed annotations enhance detection capabilities, enabling the development of more accurate and reliable models for complex environments. This comprehensive dataset is versatile, suitable for applications ranging from autonomous driving to surveillance, and has significantly improved object detection performance, resulting in higher accuracy and robustness in challenging scenarios. Using this dataset, our study achieved significant results with the YOLOv7 model. The model demonstrated high accuracy in detecting various vehicle types, even under challenging conditions. The results highlight the effectiveness of the dataset in training robust vehicle detection models and underscore its potential for future research and development in this field. Our comparative analysis evaluated YOLOv7 against its variants, YOLOv7x and YOLOv7-tiny, using both the “ISTraffic” dataset and the COCO (Common Objects in Context) benchmark. YOLOv7x outperformed others with a mAP@0.5 of 0.87, precision of 0.89, and recall of 0.84, showing a 35% performance improvement over COCO. Performance varied under different conditions, with daytime yielding higher accuracy compared to night-time and rainy weather, where vehicle headlights affected object contours. Despite effective vehicle detection and counting, tracking high-speed vehicles remains a challenge. Additionally, the algorithm’s deep learning estimates of emissions (CO, NO, NO<sub>2</sub>, NO<sub>x</sub>, PM<sub>2.5</sub>, and PM<sub>10</sub>) were 7.7% to 10.1% lower than ground-truth. |
| format | Article |
| id | doaj-art-ddce0aa4d97d44d78a25825168b7cd67 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-ddce0aa4d97d44d78a25825168b7cd672024-12-13T16:23:03ZengMDPI AGApplied Sciences2076-34172024-11-0114231117510.3390/app142311175Development of Artificial Intelligent-Based Methodology to Prepare Input for Estimating Vehicle EmissionsElif Yavuz0Alihan Öztürk1Nedime Gaye Nur Balkanlı2Şeref Naci Engin3S. Levent Kuzu4Environmental Engineering Department, Civil Engineering Faculty, Yildiz Technical University, 34220 Istanbul, TürkiyeFaculty of Electrical & Electronics, Control and Automation Engineering Department, Yildiz Technical University, 34220 Istanbul, TürkiyeEnvironmental Engineering Department, Civil Engineering Faculty, Yildiz Technical University, 34220 Istanbul, TürkiyeFaculty of Electrical & Electronics, Control and Automation Engineering Department, Yildiz Technical University, 34220 Istanbul, TürkiyeEnvironmental Engineering Department, Civil Engineering Faculty, Istanbul Technical University, 34467 Istanbul, TürkiyeMachine learning has significantly advanced traffic surveillance and management, with YOLO (You Only Look Once) being a prominent Convolutional Neural Network (CNN) algorithm for vehicle detection. This study utilizes YOLO version 7 (YOLOv7) combined with the Kalman-based SORT (Simple Online and Real-time Tracking) algorithm as one of the models used in our experiments for real-time vehicle identification. We developed the “ISTraffic” dataset. We have also included an overview of existing datasets in the domain of vehicle detection, highlighting their shortcomings: existing vehicle detection datasets often have incomplete annotations and limited diversity, but our “ISTraffic” dataset addresses these issues with detailed and extensive annotations for higher accuracy and robustness. The ISTraffic dataset is meticulously annotated, ensuring high-quality labels for every visible object, including those that are truncated, obscured, or extremely small. With 36,841 annotated examples and an average of 32.7 annotations per image, it offers extensive coverage and dense annotations, making it highly valuable for various object detection and tracking applications. The detailed annotations enhance detection capabilities, enabling the development of more accurate and reliable models for complex environments. This comprehensive dataset is versatile, suitable for applications ranging from autonomous driving to surveillance, and has significantly improved object detection performance, resulting in higher accuracy and robustness in challenging scenarios. Using this dataset, our study achieved significant results with the YOLOv7 model. The model demonstrated high accuracy in detecting various vehicle types, even under challenging conditions. The results highlight the effectiveness of the dataset in training robust vehicle detection models and underscore its potential for future research and development in this field. Our comparative analysis evaluated YOLOv7 against its variants, YOLOv7x and YOLOv7-tiny, using both the “ISTraffic” dataset and the COCO (Common Objects in Context) benchmark. YOLOv7x outperformed others with a mAP@0.5 of 0.87, precision of 0.89, and recall of 0.84, showing a 35% performance improvement over COCO. Performance varied under different conditions, with daytime yielding higher accuracy compared to night-time and rainy weather, where vehicle headlights affected object contours. Despite effective vehicle detection and counting, tracking high-speed vehicles remains a challenge. Additionally, the algorithm’s deep learning estimates of emissions (CO, NO, NO<sub>2</sub>, NO<sub>x</sub>, PM<sub>2.5</sub>, and PM<sub>10</sub>) were 7.7% to 10.1% lower than ground-truth.https://www.mdpi.com/2076-3417/14/23/11175machine learningvehicle detectionYOLOv7SORTemission |
| spellingShingle | Elif Yavuz Alihan Öztürk Nedime Gaye Nur Balkanlı Şeref Naci Engin S. Levent Kuzu Development of Artificial Intelligent-Based Methodology to Prepare Input for Estimating Vehicle Emissions Applied Sciences machine learning vehicle detection YOLOv7 SORT emission |
| title | Development of Artificial Intelligent-Based Methodology to Prepare Input for Estimating Vehicle Emissions |
| title_full | Development of Artificial Intelligent-Based Methodology to Prepare Input for Estimating Vehicle Emissions |
| title_fullStr | Development of Artificial Intelligent-Based Methodology to Prepare Input for Estimating Vehicle Emissions |
| title_full_unstemmed | Development of Artificial Intelligent-Based Methodology to Prepare Input for Estimating Vehicle Emissions |
| title_short | Development of Artificial Intelligent-Based Methodology to Prepare Input for Estimating Vehicle Emissions |
| title_sort | development of artificial intelligent based methodology to prepare input for estimating vehicle emissions |
| topic | machine learning vehicle detection YOLOv7 SORT emission |
| url | https://www.mdpi.com/2076-3417/14/23/11175 |
| work_keys_str_mv | AT elifyavuz developmentofartificialintelligentbasedmethodologytoprepareinputforestimatingvehicleemissions AT alihanozturk developmentofartificialintelligentbasedmethodologytoprepareinputforestimatingvehicleemissions AT nedimegayenurbalkanlı developmentofartificialintelligentbasedmethodologytoprepareinputforestimatingvehicleemissions AT serefnaciengin developmentofartificialintelligentbasedmethodologytoprepareinputforestimatingvehicleemissions AT sleventkuzu developmentofartificialintelligentbasedmethodologytoprepareinputforestimatingvehicleemissions |