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Ahmad Azhari
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simple@ascee.org
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Jl. Raya Janti No.130B, Karang Janbe, Karangjambe, Kec. Banguntapan, Kabupaten Bantul, Daerah Istimewa Yogyakarta 55198
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INDONESIA
Signal and Image Processing Letters
Published by Association For Scientific Computing Electrical and Engineering
ISSN : 27146669     EISSN : 27146677     DOI : 10.31763/simple
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering
The journal invites original, significant, and rigorous inquiry into all subjects within or across disciplines related to signal processing and image processing. It encourages debate and cross-disciplinary exchange across a broad range of approaches.
Arjuna Subject : Ilmu Komputer (semua kategori) - Pemrosesan Sinyal
Articles 2 Documents
 1 
Search results for , issue "Vol 7, No 2 (2025)" : 2 Documents clear
Vehicle Detection and Tracking using Coarse-to-Fine Module and Spatial Pyramid Pooling–Fast with Deep Sort Saputri, Anita Nur Widdia; Hendrawan, Aria; Khoiriyah, Rofiatul
Signal and Image Processing Letters Vol 7, No 2 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i2.118

Abstract

Semarang City, a rapidly growing urban area in Indonesia, faces significant traffic challenges stemming from the widespread use of motorcycles, an inefficient public transportation system, and accelerated urban development. These factors contribute to congestion and complicate traffic management efforts. To address this issue and enhance monitoring capabilities, this study develops an automatic vehicle detection system utilizing the YOLOv8 algorithm, applied to CCTV footage obtained from TILIK SEMAR, a local traffic surveillance initiative. The research methodology encompasses several key stages: data collection from real-world traffic scenarios, meticulous annotation of vehicle types, model training using the YOLOv8 framework, and performance evaluation conducted at two distinct locations in Semarang—Banyumanik and Thamrin Pandanaran. The trained model achieved an impressive average accuracy, measured as mean Average Precision (mAP50), exceeding 97%, with a rapid processing time of 4.2 milliseconds per image, making it suitable for real-time applications. Among vehicle categories, the highest detection accuracies were recorded for buses at 99.3% and box trucks at 99.5%, reflecting the model’s robustness for larger vehicles. However, motorcycles presented a challenge, with a lower mAP50-95 score of 64.3%, attributed to variations in shape, size, and lighting conditions. Overall, the system successfully identified 96.77% of 3,036 vehicles across the test dataset, demonstrating strong generalization across diverse traffic conditions. These findings validate YOLOv8 as an effective tool for real-time traffic monitoring in urban settings. Future enhancements will focus on expanding dataset diversity and improving performance under challenging environmental factors, such as adverse weather or low-light scenarios, to further refine the system’s reliability.
Agricultural Mechatronics: Orange Sorting System Using Image Segmentation Fathurrahman, Haris Imam Karim; Aulia, Imam Haris
Signal and Image Processing Letters Vol 7, No 2 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i2.142

Abstract

Sorting oranges after harvest is a critical step. It requires separating ripe fruit from unripe. Traditionally, this is done by hand. This method is inefficient and subjective. It is not suitable for modern agriculture. This study creates an automated system to solve this problem. The system uses mechatronics and image processing. Its core uses the HSV color space for image analysis. This method is effective for assessing the peel's color, which indicates maturity. The mechatronic system performs the physical sorting using a servo motor. It includes a conveyor belt, a digital camera, a processing unit, and an actuator. This research was tested on 30 sample oranges. The results show 90% accuracy in mechatronics sorting. This proves the system is a reliable and effective tool for quality control.

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