Performance comparison of novel object detection models on traffic data
Open Access
- 10 March 2023
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 177-184
Abstract
This research evaluated the performance of 14 novel object detection models on a traffic dataset to reveal significant differences between the selected models. The goal was to generate recommendations for best-performing models for researchers coming into the domain. The performance is evaluated with respect to the size of objects, inference time, and the number of learnable parameters. The results show that even though several models have similar performance on the entire dataset, their performance on small and large objects can significantly differ. Also, the difference in inference time is significant and could influence the choice of a suitable model. The paper’s conclusion emphasizes the best-performing models based on their performance strengths.Keywords
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