How to use SAHI to detect smaller objects...!

In the world of computer vision, detecting small objects within images has always been a challenge. These tiny elements can easily be overshadowed by larger objects or get lost in the complexity of an image. However, the solution to this problem might lie in an innovative approach known as Slicing Aided Hyper Inference (SAHI). In this article, we will explore how SAHI, in combination with Robo flow(online labeling tool) and supervision, can significantly improve small object detection in computer vision.

Step 1: Data Preparation

Before diving into SAHI, it's crucial to start with high-quality annotated data. Robo flow is a fantastic platform that simplifies the process of data preparation. You can upload your dataset, annotate images, and easily generate data augmentation techniques to ensure your model has a diverse set of examples to learn from.

Step 2: Model Selection

Choose a suitable model for your task. While SAHI is model-agnostic, selecting a model that supports supervision data loaders is essential. Popular options include YOLO (You Only Look Once) try using YOLOv8, Faster R-CNN, or EfficientDet. Depending on your specific use case and resource constraints, you can opt for a pre-trained model or train one from scratch.

Step 3: Implementing SAHI

Now comes the exciting part—implementing SAHI into your object detection pipeline. Break down your input image into smaller slices or tiles. These slices should overlap to ensure that no small object goes unnoticed. Run inference on each slice independently using your selected model.

Step 4: Aggregation

Once inference is complete for all slices, it's time to aggregate the results. Combine the predictions from each slice to create a comprehensive detection map. This map will reveal the presence and location of small objects across the entire image.

Step 5: Post-processing

To refine your results, you may need to perform post-processing steps, such as non-maximum suppression or filtering based on confidence scores. These techniques will help eliminate duplicate or false positive detections, leaving you with a more accurate final output.

Step 6: Evaluation and Iteration

Evaluate the performance of your SAHI-based model using standard metrics like precision, recall, and F1 score. Fine-tune your model as needed, iterating through the process until you achieve the desired level of small object detection accuracy.

Please follow Tarunbalaji Srinivasan for the part 2 which let you to know about the benifits of using SAHI.