Obstacle detection Computer Vision Dataset
About Obstacle detection Dataset
Here are a few use cases for this project:
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Autonomous Vehicle Navigation: This model can be used as a part of autonomous vehicle systems for the detection and avoidance of obstacles while navigating through streets. It can assist the self-driving vehicle to classify different obstacles such as a humans, dogs, cars, motorcycles, bicycles, trams, buses, trees, traffic signs, electric poles, and even uncovered manholes.
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Security Surveillance: It can be implemented in security cameras for identifying possible obstructions or threats, including unauthorized persons, suspicious vehicles, or uncovered manholes.
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Smart Cities Infrastructure Planning: Local municipality or city planning departments can use the model to monitor and maintain urban infrastructure such as traffic signs, electric poles or identifying potential public safety hazards like uncovered manholes.
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Assisting Visually Impaired People: The model can be incorporated into assistive technology for visually impaired people. By detecting obstacles in their path (cars, trees, uncovered manholes, etc.), the model could help guide them safely through urban spaces.
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Drones and Robotic Delivery Systems: For airborne drones or ground-based robotic delivery systems, identifying and avoiding obstacles is crucial. The model could provide real-time information about potential obstructions, ensuring safe and efficient delivery of packages.
Use Free Car, Bus and Dog Detection API
Powered by general detection model
pip install inference-sdk# 1. Import the library
from inference_sdk import InferenceHTTPClient
# 2. Connect to your workspace
client = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="API_KEY"
)
# 3. Run your workflow on an image
result = client.run_workflow(
workspace_name="<YOUR_WORKSPACE>",
workflow_id="<YOUR_WORKFLOW_ID>",
images={
"image": "YOUR_IMAGE.jpg" # Path to your image file
},
parameters={
"classes": "Car, Bus, Dog, Bicycle, Motorcycle"
},
use_cache=True # cache workflow definition for 15 minutes
)
# 4. Get your results
print(result)Run on custom image
Drop an image here or click to upload
Tell the agent what you want to build.
Cite This Project
LicenseCC BY 4.0If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ obstacle-detection-yeuzf-wyagl_dataset,
title = { Obstacle detection Dataset },
type = { Open Source Dataset },
author = { ML First },
howpublished = { \url{ https://universe.roboflow.com/ml-first/obstacle-detection-yeuzf-wyagl } },
url = { https://universe.roboflow.com/ml-first/obstacle-detection-yeuzf-wyagl },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2025 },
month = { jan },
note = { visited on 2026-07-29 },
}