Improving Vehicle Detection in Challenging Datasets: YOLOv5sand Frozen Layers Analysis

Dublin Core

Title

Improving Vehicle Detection in Challenging Datasets: YOLOv5sand Frozen Layers Analysis

Subject

YOLOv5s, Image Detection, Transfer Learning, Imbalance Dataset, CNN

Description

Small datasets and imbalanced classes often cause problems when used as primary research material. In the case of classification and object detection, some researchers proposed Transfer Learning (TF) with several frozen layers. Moreover, YOLO (You Only Look Once) is one of the algorithms that works in real-time object detection. In this research, we focused on evaluating the YOLOv5s version of detecting vehicles in small and imbalanced datasets. The original YOLOv5s were trained and compared with YOLOv5s with the freezing layers method (10 and 24 frozen layers). The experimental results of original YOLOv5s were precision score of 0.779, recall value of 0.933, [email protected] of 0.93 and [email protected]:0.95 of 0.684 while YOLOv5s with 10 frozen layers where precision score was decreased to 0.639, but the other value increase with recall value of 0.939, [email protected] of 0.951 and [email protected]:0.95 of 0.732. Overall, the version with 10 frozen layers demonstrated superior performance in addressing the challenges of small and imbalanced datasets, particularly excelling in recall and mAP metrics

Creator

Ahmad Nanda Yuma Rafi1, Mohamad Yusuf

Source

https://ijicom.respati.ac.id/index.php/ijicom/article/view/64/51

Date

December 2023

Contributor

Fajar bagus W

Format

PDF

Language

English

Type

Text

Files

Collection

Citation

Ahmad Nanda Yuma Rafi1, Mohamad Yusuf, “Improving Vehicle Detection in Challenging Datasets: YOLOv5sand Frozen Layers Analysis,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8390.