## 4 Discussion  
The results indicate that the \"contrast\" technique yields the best performance on the modified DO-SMA dataset, which simulates dark conditions (Figure 12). The \"equalize\" technique also shows strong results on modified images. It should be noted that these techniques alter the color distribution. The models trained on the original dataset, which has a close to normal distribution of average image brightness, were validated on the DO-SMA dataset with modified brightness. This suggests that the \"contrast\" and \"equalize\" augmentations improve the generalization ability of the models. The issue is that variations in lighting and exposure or camera settings affect color and saturation distribution in real-world photos, making it challenging for DNN models to make accurate predictions. The \"contrast\" augmentation calculates a histogram of the input image, removes a percentage of the lightest and darkest pixels from the histogram according to the cutoff value, and remaps the image so that the darkest pixel becomes black (0) and the lightest becomes white (255). The \"equalize\" augmentation redistributes grayscale values to create a uniform histogram. These functions help the model handle external variations in color distribution across the additional channels. The \"contrast\" transformation ignores the brightest or darkest pixels and changes the distribution to focus on dark or bright features, respectively. In comparison to spatial augmentations (\"move\"), which do not affect color distribution, color channel augmentations show better results in simulated dark conditions. The \"move\" technique, which shifts the image and is a spatial augmentation, performs best on the original validation dataset. This is likely because moving parts of the image help the model focus on different features and learn features in various locations. Real-world images under varying light conditions often exhibit saturation distributions similar to the modified DO-SMA dataset (Figure 13). The application of the \"contrast\" augmentation can normalize data across channels, making the data more consistent despite different lighting conditions. This approach improves detail detection for the human eye, even highlighting different aspects such as objects or text, depending on the image. In the case of the \"contrast\" augmentation, one of the images focuses on details in the dark and another on details in the light. The same results can be demonstrated on real-world images in different light conditions (Figure 14), where by using different images, it is easier to  
Figure 12: Examples of images with average brightness level modified by factors of 0.2, 0.4, 0.6, 0.8, and 1.0 and their saturation histograms. detect different kinds of details. In the image with the \"contrast\" augmentation applied with parameters (0, 40), it is easier for the human eye to read text and see pedestrians. On the other hand, in the image with the \"contrast\" augmentation applied with parameters (40, 0), it is easier to detect sources of light, like stop lights on cars or traffic lights. The fusion of the model output results can help to detect different kinds of features and improve the overall performance of the models. In addition, the contrast adjustment used in the SMA technique helps mitigate overfitting to the dataset's lighting conditions by transforming the input images, allowing the model to learn independent of the lighting characteristics. As a result, the model receives multiple augmented inputs instead of just one. While the initial input may be influenced by varying lighting conditions, the contrast-adjusted versions help the model produce light-insensitive representations. This enables the model to achieve better performance on images with simulated low-lighting conditions, thus reducing the risk of overfitting to specific data distribution characteristics.