Summary of A Recurrent Yolov8-based Framework For Event-based Object Detection, by Diego A. Silva et al.
A Recurrent YOLOv8-based framework for Event-Based Object Detection
by Diego A. Silva, Kamilya Smagulova, Ahmed Elsheikh, Mohammed E. Fouda, Ahmed M. Eltawil
First submitted to arxiv on: 9 Aug 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 proposed ReYOLOv8 framework enhances a leading frame-based object detection system by incorporating spatiotemporal modeling capabilities, specifically designed for event-based cameras. This enables superior performance in environments with fast motion and extreme lighting conditions while reducing power consumption. The framework’s low-latency and memory-efficient encoding method boosts performance further. Novel data augmentation techniques tailored to event data improve detection accuracy. Experimental results on the GEN1 dataset (automotive applications) show mean Average Precision (mAP) improvements of 5%, 2.8%, and 2.5% across nano, small, and medium scales, respectively. This is achieved with an average reduction in trainable parameters by 4.43% and real-time processing speeds ranging from 9.2ms to 15.5ms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Object detection helps self-driving cars and robots see the world around them. Right now, most cameras use a “frame-based” approach, which can struggle with blurry images and poor lighting. Event-based cameras are different – they mimic how our eyes work and perform better in these conditions while using less power. The new ReYOLOv8 framework combines the best of both worlds to improve object detection even more. It uses special techniques to analyze event data and make decisions faster. In tests on automotive and robotics datasets, this approach showed significant improvements (5-18%) with smaller models that can process information in real-time. |
Keywords
» Artificial intelligence » Data augmentation » Mean average precision » Object detection » Spatiotemporal
