TY - GEN
T1 - Effects of Brightness and Class-Unbalanced Dataset on CNN Model Selection and Image Classification Considering Autonomous Driving
AU - Nazir, Zhumakhan
AU - Yarovenko, Vladislav
AU - Park, Jurn Gyu
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2024
Y1 - 2024
N2 - In addition to an approach of combining machine learning (ML) enhanced models and convolutional neural networks (CNNs) for adaptive CNN model selection, a thorough investigation study of the effects of 1) image brightness and 2) class-balanced/-unbalanced datasets is needed, considering image classification (and object detection) for autonomous driving in significantly different daytime and nighttime settings. In this empirical study, we comprehensively investigate the effects of these two main issues on CNN performance by using the ImageNet dataset, predictive models (premodel), and CNN models. Based on the experimental results and analysis, we reveal non-trivial pitfalls (up to 58% difference in top-1 accuracy in different class-balance datasets) and opportunities in classification accuracy by changing brightness levels and class-balance ratios in datasets.
AB - In addition to an approach of combining machine learning (ML) enhanced models and convolutional neural networks (CNNs) for adaptive CNN model selection, a thorough investigation study of the effects of 1) image brightness and 2) class-balanced/-unbalanced datasets is needed, considering image classification (and object detection) for autonomous driving in significantly different daytime and nighttime settings. In this empirical study, we comprehensively investigate the effects of these two main issues on CNN performance by using the ImageNet dataset, predictive models (premodel), and CNN models. Based on the experimental results and analysis, we reveal non-trivial pitfalls (up to 58% difference in top-1 accuracy in different class-balance datasets) and opportunities in classification accuracy by changing brightness levels and class-balance ratios in datasets.
KW - Balanced Dataset
KW - CNNs
KW - Image Feature Extraction
KW - Interpretable Models
UR - http://www.scopus.com/inward/record.url?scp=85178648529&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85178648529&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-8184-7_15
DO - 10.1007/978-981-99-8184-7_15
M3 - Conference contribution
AN - SCOPUS:85178648529
SN - 9789819981830
T3 - Communications in Computer and Information Science
SP - 191
EP - 203
BT - Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings
A2 - Luo, Biao
A2 - Cheng, Long
A2 - Wu, Zheng-Guang
A2 - Li, Hongyi
A2 - Li, Chaojie
PB - Springer Science and Business Media Deutschland GmbH
T2 - 30th International Conference on Neural Information Processing, ICONIP 2023
Y2 - 20 November 2023 through 23 November 2023
ER -