Summary of Soar: Advancements in Small Body Object Detection For Aerial Imagery Using State Space Models and Programmable Gradients, by Tushar Verma et al.
SOAR: Advancements in Small Body Object Detection for Aerial Imagery Using State Space Models and Programmable Gradients
by Tushar Verma, Jyotsna Singh, Yash Bhartari, Rishi Jarwal, Suraj Singh, Shubhkarman Singh
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces two innovative approaches to enhance small object detection and segmentation capabilities in aerial imagery, a challenging task due to minimal data and occlusion by larger objects and noise. Traditional transformer-based models are limited by the lack of specialized databases, affecting their performance with varying orientations and scales. The proposed methods include the SAHI framework on YOLO v9 architecture, utilizing Programmable Gradient Information (PGI) to reduce information loss in sequential feature extraction. Additionally, the Vision Mamba model incorporates position embeddings for precise location-aware visual understanding and a novel bidirectional State Space Model (SSM) for effective visual context modeling. The SSM leverages the linear complexity of CNNs and global receptive field of Transformers, making it suitable for remote sensing image classification. Experimental results show significant improvements in detection accuracy and processing efficiency, validating the approaches’ applicability for real-time small object detection across diverse aerial scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces two new ways to improve finding small objects in aerial images. This is a tough task because there’s not much data available and small objects are often hidden by larger ones or noise. Traditional models that use transformers have limitations due to the lack of special databases, which makes them less accurate when dealing with objects at different angles and sizes. The new methods include using SAHI on YOLO v9, which reduces information loss during feature extraction, and a new model called Vision Mamba that uses position embeddings for better location-based understanding and a state space model for contextualizing visual data. These approaches can be used in real-time object detection and could lead to future advancements in aerial image recognition. |
Keywords
* Artificial intelligence * Feature extraction * Image classification * Object detection * Transformer * Yolo
