YolactEdge: Real-time Instance Segmentation on the Edge
Abstract
We propose YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on a Jetson AGX Xavier (and 172.7 FPS on an RTX 2080 Ti) with a ResNet-101 backbone on 550x550 resolution images. To achieve this, we make two improvements to the state-of-the-art image-based real-time method YOLACT [1]: (1) applying TensorRT optimization while carefully trading off speed and accuracy, and (2) a novel feature warping module to exploit temporal redundancy in videos. Experiments on the YouTube VIS and MS COCO datasets demonstrate that YolactEdge produces a 3-5x speed up over existing real-time methods while producing competitive mask and box detection accuracy. We also conduct ablation studies to dissect our design choices and modules. Code and models are available at https://github.com/haotian-liu/yolact_edge.
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Event cameras are especially well-suited for situations involving a lot of motion and difficult lighting conditions.
“thanks to these properties, event cameras are especially well-suited for situations involving a lot of motion, and difficult lighting conditions [142], [143].”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [143]
Cites
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FCOS: Fully Convolutional One-Stage Object Detection
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A Systematic Review of Drone Based Road Traffic Monitoring System
Bisio, I., Garibotto, C., Haleem, H., Lavagetto, F., & Sciarrone, A. (2022) · IEEE Access · doi:10.1109/access.2022.3207282 · Cited by 121 · see citing works
“Therefore, the design of specific 1272 Yolact ++ [142], and Yolactedge [143] can also be used to From traffic monitoring perspective, the goal of MOT is to track all the vehicles in an aerial sequence.”
- Authors
- Liu, H., Rivera Soto, R. A., Xiao, F., & Jae Lee, Y.
- Affiliations
- University of California, Davis (US)
- Venue
- 2021 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2021-05-30
- Pages
- 9579-9585
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W3205388591
- Cited by
- 82
- References
- 6
- Citations by year
- 2026: 4 · 2025: 19 · 2024: 20 · 2023: 20 · 2022: 17 · 2021: 2
- FWCI
- 4.82
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- instance segmentation, real-time object detection, edge devices, YOLACT, TensorRT optimization, feature warping, temporal redundancy, NVIDIA Jetson AGX Xavier, video processing, speed-accuracy trade-off
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