Influence of Model Size and Image Augmentations on Object Detection in Low-Contrast Complex Background Scenes
Background: Bigger and more complex models are often developed for challenging object detection tasks, and image augmentations are used to train a robust deep learning model for small image datasets. Previous studies have suggested that smaller models provide better performance compared to bigger mo...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | AI |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-2688/6/3/52 |
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| Summary: | Background: Bigger and more complex models are often developed for challenging object detection tasks, and image augmentations are used to train a robust deep learning model for small image datasets. Previous studies have suggested that smaller models provide better performance compared to bigger models for agricultural applications, and not all image augmentation methods contribute equally to model performance. An important part of these studies was also to define the scene of the image. Methods: A standard definition was developed to describe scenes in real-world agricultural datasets by reviewing various image-based machine-learning applications in the agriculture literature. This study primarily evaluates the effects of model size in both one-stage and two-stage detectors on model performance for low-contrast complex background applications. It further explores the influence of different photo-metric image augmentation methods on model performance for standard one-stage and two-stage detectors. Results: For one-stage detectors, a smaller model performed better than a bigger model. Whereas in the case of two-stage detectors, model performance increased with model size. In image augmentations, some methods considerably improved model performance and some either provided no improvement or reduced the model performance in both one-stage and two-stage detectors compared to the baseline. |
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| ISSN: | 2673-2688 |