| Summary: | Dataset quality is an integral aspect of image object detection. Consequently, poor-quality datasets are often blamed for limiting model performance. Conventional convolutional neural network models attempt to address the model performance issue and have achieved high accuracy; however, these models fail to detect objects with photometric and geometric variations in similar images. To address this object detection issue, we adopt selective image augmentation by identifying potential weak points of photometric factors, such as brightness and sharpness. A stall-housed pig dataset and a food product dataset with complex and clear backgrounds, respectively, were selected to test our hypothesis. Images from both test datasets were investigated with photometric variations ranging between −60% <inline-formula> <tex-math notation="LaTeX">$\sim ~60$ </tex-math></inline-formula>% with a step size of 20% to identify the weak points of the dataset. The test results demonstrate that a photometrically balanced dataset with selective augmentation significantly outperforms the original dataset in terms of high accuracy and robustness.
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