tianyu0207/PEBAL
[ECCV'22 Oral] Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes
This project helps autonomous vehicle engineers and researchers detect unusual objects or situations on roads. It takes in standard camera images from urban driving scenes and outputs precise outlines around anything abnormal, like unexpected debris or unusual road conditions, even if the system hasn't seen them before. This is for professionals building safer and more reliable self-driving systems.
143 stars. No commits in the last 6 months.
Use this if you need to identify and segment out unknown, anomalous objects or conditions in urban driving camera feeds for autonomous vehicles.
Not ideal if your application involves anomaly detection in non-driving contexts or requires classifying specific types of anomalies.
Stars
143
Forks
19
Language
Python
License
—
Category
Last pushed
Aug 31, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/computer-vision/tianyu0207/PEBAL"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
blaz-r/SuperSimpleNet
Official implementation of SuperSimpleNet [ICPR 2024, JIMS 2025]
tianyu0207/RTFM
Official code for 'Weakly-supervised Video Anomaly Detection with Robust Temporal Feature...
tientrandinh/Revisiting-Reverse-Distillation
(CVPR 2023) Revisiting Reverse Distillation for Anomaly Detection
eliahuhorwitz/3D-ADS
Official Implementation for the "Back to the Feature: Classical 3D Features are (Almost) All You...
MaticFuc/SALAD
[ICCV 2025] SALAD -- Semantics-Aware Logical Anomaly Detection