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All Journal TEKNOLOGI INFORMASI Jurnal Edukasi dan Penelitian Informatika (JEPIN) PROtek : Jurnal Ilmiah Teknik Elektro Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro ILKOM Jurnal Ilmiah Jiko (Jurnal Informatika dan komputer) JURNAL TEKNOLOGI INFORMASI Jurnal Pendidikan dan Konseling bit-Tech JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Journal of Education Research Jurnal Pengabdian Pada Masyarakat Indonesia (JPPMI) Jurnal Ilmiah Multidisiplin (JUKIM) NEAR: Jurnal Pengabdian kepada Masyarakat
mohamad jamil
Teknik Informatika Universitas Khairun
Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Transportation Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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A Comparative Evaluation of YOLOv9 and DETR Models in Traffic Object Detection for Intelligent Surveillance Systems Jamil, Mohamad; Nagu, Nani; Said, Muhammad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6556

Abstract

Object detection plays a crucial role in traffic surveillance, particularly in urban environments characterized by high vehicle density, diverse weather conditions, and limited computational resources. Although YOLOv9 and DETR have demonstrated strong performance in general object detection tasks, there is a lack of comparative research evaluating their effectiveness under specific challenges of traffic surveillance. These challenges include the need for real-time processing, accurate detection of small or partially occluded objects, and adaptability to complex traffic scenarios. This study addresses this gap by conducting a comparative evaluation of YOLOv9 and DETR using a custom traffic image dataset, with training iterations varied from 10 to 50 epochs to observe performance development. Evaluation metrics included mean average precision, precision, recall, F1-score, inference time, and object count per image. The results indicated that DETR achieved the highest accuracy across all metrics at the final training stage and detected up to 22 objects per image. However, the average inference time exceeded seven seconds per image, limiting the real-time applicability. Conversely, YOLOv9 achieved competitive accuracy with a significantly faster inference time of approximately 0.43 seconds per image. These findings provide practical insights into the trade-off between accuracy and processing efficiency, and offer guidance for model selection in operational traffic surveillance systems.
Co-Authors Abdul Fatah ACHMAD FUAD Adi Wibowo Amal Khairan Amali, Utdin Furqon Budi Warsito Eko Purnomo Fera The Gani, Ilham Husen Alting Jamalun Togubu Jamalun Togubu, Jamalun Karim, Kodrat Hi Kiswanto Kiswanto Misrina Sergie Mohammad Ridwan Lessy, Mohammad Ridwan Mufti Amir Sultan, Mufti Amir Muhamad Said Muhamad, Syahril MUHAMMAD SAID Nani Nagu Rosihan Rosihan, Rosihan S Hanafi, Masdar Said, Muhamad Samsu Somadayo, Samsu Sardju, Achmad Sherly Asriany Susanto Usman