Summary of Valo: a Versatile Anytime Framework For Lidar-based Object Detection Deep Neural Networks, by Ahmet Soyyigit et al.
VALO: A Versatile Anytime Framework for LiDAR-based Object Detection Deep Neural Networks
by Ahmet Soyyigit, Shuochao Yao, Heechul Yun
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This research paper presents a solution to the challenge of adapting dynamic deadline requirements for LiDAR object detection deep neural networks (DNNs). The authors aim to reduce the latency of object detection, which is critical for ensuring safe and efficient navigation. State-of-the-art LiDAR object detection DNNs often exhibit significant latency, making it challenging to achieve real-time performance on resource-constrained edge platforms. To address this issue, the paper proposes a method that dynamically manages the tradeoff between detection accuracy and latency at runtime. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about finding a way to make computer programs faster for detecting objects using special sensors called LiDAR. The goal is to make these programs work better on devices with limited power, like those used in self-driving cars. Right now, these programs take too long to process information and can’t work quickly enough. To solve this problem, the researchers want to find a way to balance how well the program works and how fast it runs. |
Keywords
» Artificial intelligence » Object detection
