| Autor: |
Hao Lin, Ashkan Parsi, Darragh Mullins, Jonathan Horgan, Enda Ward, Ciaran Eising, Patrick Denny, Brian Deegan, Martin Glavin, Edward Jones |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Journal of Imaging, Vol 10, Iss 7, p 153 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2313-433X |
| DOI: |
10.3390/jimaging10070153 |
| Popis: |
In recent years, significant advances have been made in the development of Advanced Driver Assistance Systems (ADAS) and other technology for autonomous vehicles. Automated object detection is a crucial component of autonomous driving; however, there are still known issues that affect its performance. For automotive applications, object detection algorithms are required to perform at a high standard in all lighting conditions; however, a major problem for object detection is poor performance in low-light conditions due to objects being less visible. This study considers the impact of training data composition on object detection performance in low-light conditions. In particular, this study evaluates the effect of different combinations of images of outdoor scenes, from different times of day, on the performance of deep neural networks, and considers the different challenges encountered during the training of a neural network. Through experiments with a widely used public database, as well as a number of commonly used object detection architectures, we show that more robust performance can be obtained with an appropriate balance of classes and illumination levels in the training data. The results also highlight the potential of adding images obtained in dusk and dawn conditions for improving object detection performance in day and night. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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