TrafficCamNet
TrafficCamNet is an object detection model that identifies cars, persons, road signs, and two-wheelers within images. It uses the NVIDIA DetectNet_v2 detector with ResNet18 as a feature extractor and returns a bounding box around each object with a category label. With a precision of 92.65%, recall of 89.95%, and accuracy of 83.9%, it's suitable for smart city applications like traffic pattern analysis. However, it has limitations, such as struggling with small, occluded, or nighttime objects. Can it effectively detect objects in your specific use case?
Deploy Model in Dataloop Pipelines
TrafficCamNet fits right into a Dataloop Console pipeline, making it easy to process and manage data at scale. It runs smoothly as part of a larger workflow, handling tasks like annotation, filtering, and deployment without extra hassle. Whether it's a single step or a full pipeline, it connects with other nodes easily, keeping everything running without slowdowns or manual work.
Table of Contents
Model Overview
Meet the TrafficCamNet model, designed to detect objects like cars, people, road signs, and two-wheelers in images. But how does it work?
What can it do?
- Detect one or more physical objects from four categories: cars, persons, road signs, and two-wheelers
- Return a bounding box around each object and a category label for each object
How does it work?
The model uses the NVIDIA DetectNet_v2 detector with ResNet18 as a feature extractor. This architecture, also known as GridBox object detection, uses bounding-box regression on a uniform grid on the input image. Think of it like dividing an image into a grid and predicting where objects are and what they are.
Performance
The model has been tested on 19,000 images and has shown:
- Precision: 92.65%
- Recall: 89.95%
- Accuracy: 83.9%
However, it’s not perfect and has some limitations, such as:
- Trouble detecting very small objects
- Trouble detecting occluded objects
- Trouble detecting objects in night-time, monochrome, or infrared camera images
Capabilities
The TrafficCamNet model is designed to detect one or more physical objects from four categories within an image: cars, persons, road signs, and two-wheelers. But what does that really mean?
- Can you imagine being able to automatically identify cars, people, and road signs in a photo or video? That’s what this model can do!
- It returns a bounding box around each object and a category label for each object. Think of it like a virtual highlighter that draws a box around each object and labels it.
Potential Applications
So, what can you use this model for? The primary use case is detecting cars in color images, but it can also be used to detect people, road signs, and two-wheelers.
- Imagine using this model in smart city applications, like analyzing traffic patterns or identifying anomalous trajectories.
- You could even use it to detect objects in photos and videos, like a virtual detective.
Limitations
While the TrafficCamNet model performs well in many areas, it does have some limitations. For example:
- It struggles to detect very small objects
- It has difficulty detecting occluded objects
- It is not effective in detecting objects in night-time, monochrome, or infrared camera images
These limitations are important to consider when deciding whether to use the TrafficCamNet model for a particular application.
Format
TrafficCamNet is a powerful model that detects physical objects in images. But, how does it work? Let’s break it down.
Architecture
TrafficCamNet is based on the NVIDIA DetectNet_v2 detector with ResNet18 as a feature extractor. This architecture is also known as GridBox object detection. So, what does that mean? In simple terms, the model divides an input image into a grid and predicts the location and type of objects within that grid.
Data Formats
TrafficCamNet accepts input images in RGB format. That’s right, just like the photos you take with your smartphone! The model can handle images of various sizes, but it’s designed to work best with images that are at least 1.8M pixels.
Input Requirements
To use TrafficCamNet, you’ll need to pre-process your input images. Here’s an example of how to do it:
import cv2
# Load the image
img = cv2.imread('image.jpg')
# Resize the image to the desired size
img = cv2.resize(img, (1024, 768))
# Convert the image to RGB format
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
Output Format
The model returns a list of detected objects, each with a bounding box and a category label. The output format is as follows:
[
{
"class": "car",
"confidence": 0.8,
"x": 100,
"y": 200,
"w": 300,
"h": 400
},
{
"class": "person",
"confidence": 0.9,
"x": 500,
"y": 300,
"w": 200,
"h": 300
}
]
