Understanding Image Scale Augmentation
Image Scale Augmentation is a technique that is used to augment images through which we randomly select the short size of an image from within a specific dimensional range. The augmentation technique is widely used in various computer vision applications like image classification, recognition, and detection.
Image augmentation is a technique of modifying images to create new data from the original data. This technique is used to increase the amount and variety of training data without collecting new images manually. Image augmentation techniques include operations like flipping, rotating, translating, cropping, and contrast adjustment.
With the increasing use of deep learning models in image recognition, classification, and detection, the need for large amounts of training data has tremendously increased. Image Scale Augmentation is an effective technique used to create more training data from the existing images. It is especially useful in object detection where we want our model to detect objects of varying sizes and scales.
The Need for Image Scale Augmentation
In object detection, we use machines to locate the position of objects in images. The machine scans the image and identifies the locations of objects present in the image. However, identifying objects in an image is not an easy task. An image can have multiple objects of different sizes and scales, which makes it challenging to detect them accurately.
For instance, consider an image of a traffic scene that contains cars of different sizes. Detecting all the cars in the image can be a challenging task. If the machine is trained with smaller sized images, it will not be able to detect larger sized cars in the image. Likewise, if the machine is trained with larger sized images, it will not be able to detect smaller cars in the image. Therefore, it is important to train the machine with images of varying sizes and scales, which is where Image Scale Augmentation comes in.
Image Scale Augmentation helps in training the machine to detect objects of varying sizes and scales. By randomly selecting the short size of the image during training, we can train the machine to recognize objects of different sizes in the image. This helps in improving the accuracy of object detection models by training them on a diverse set of images.
How Image Scale Augmentation Works
In Image Scale Augmentation, we randomly pick the short size of an image within a dimensional range. We maintain a minimum and maximum dimension range and randomly pick the short side from within this range. The long side dimension is calculated by maintaining the original aspect ratio of the image.
For instance, if we are training our machine with images of dimensions ranging from 400X400 to 800X800, we randomly pick a short side that falls within this dimensional range. The long side is calculated based on the aspect ratio of the original image. Therefore, if the original image has an aspect ratio of 2:1, the long side will be calculated as 800X400.
During training, we apply Image Scale Augmentation to each image before feeding it to the model. This creates multiple versions of the same image with varying scales and sizes, which helps in training the machine to detect objects of varying sizes and scales.
Benefits of Image Scale Augmentation
Image Scale Augmentation has numerous benefits in computer vision applications. Some of the benefits include:
- Improved Accuracy: Image Scale Augmentation helps in improving the accuracy of object detection models by training them on a diverse set of images of varying sizes and scales.
- Increased Efficiency: Image Scale Augmentation helps in creating more training data from the existing images without the need for manual collection of new images. This increases the efficiency of the training process by reducing the time and effort required to collect new data.
- Reduced Overfitting: Overfitting occurs when the machine is trained with a limited set of data and fails to generalize to new, unseen data. Image Scale Augmentation helps in reducing overfitting by training the machine on a diverse set of images of varying sizes and scales.
Limitations of Image Scale Augmentation
Although Image Scale Augmentation has numerous benefits, there are certain limitations to the technique as well. Some limitations include:
- Increased Training Time: Image Scale Augmentation increases the number of images used for training, which in turn increases the time required for training the machine.
- Increased Memory: Each version of the image created using Image Scale Augmentation takes up memory, which can become a problem when dealing with large datasets.
- Decreased Quality: Image Scale Augmentation can decrease the quality of the image, especially when the short side of the image is significantly smaller than the original image.
In Conclusion
Image Scale Augmentation is a powerful technique used to augment images for computer vision applications like object detection. By randomly selecting the short size of an image within a dimensional range, we can train machines to detect objects of varying sizes and scales. Although Image Scale Augmentation has limitations, its benefits outweigh its limitations, making it a useful augmentation technique.
