OBJECT DETECTION IN INDONESIAN MIXED TRAFFIC FOR AUTONOMOUS VEHICLES BASED ON YOLOV5 ALGORITHM
Object detection speed and accuracy are critical aspects of the perception system in autonomous vehicles. Speed improvement helps the object detector model achieve performance close to real-time, while accuracy enhancement ensures robust detection across various scenes. Balancing these improvemen...
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| Main Author: | |
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/85207 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Object detection speed and accuracy are critical aspects of the perception system
in autonomous vehicles. Speed improvement helps the object detector model
achieve performance close to real-time, while accuracy enhancement ensures
robust detection across various scenes. Balancing these improvements enhances
the safety of autonomous vehicles, particularly in mixed and dense traffic
conditions, such as those in Indonesia. This study develops a detection model using
local datasets to reflect real-world conditions. We achieved balanced
improvements in frames per second (fps) and mean Average Precision (mAP@50-
95) through a modified YOLOv5 deep learning model. The key enhancements of the
model include the integration of GhostConv and Transformer layers into the
YOLOv5 architecture, referred to as YOLOv5s-GT, as well as the application of
image augmentations with various scenarios to the training data. Experimental
results show that the modified YOLOv5 outperformed the baseline model in both
fps and mAP metrics, achieving 82.6 fps and 80.1% mAP@50-95, compared to the
baseline performance of 75.8 fps and 77.7% mAP@50-95. |
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