Why is the Method for Object Detection and Classification So Important in AI Cameras and Sensors?

In this article, you will learn about the important role that object detection plays in efficient and accurate image and data collection, specifically in the context of traffic management. We will define the two key types of object detection models used in AI cameras and sensors and explain and compare the performance and features of open-source versus proprietary models.

What is ‘Object Detection’ in AI Cameras?

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FLIR AI-powered thermal traffic camera footage

Object detection is the field of computer vision that deals with the localization and classification of objects contained in an image or video. To put it simply: Object detection comes down to drawing bounding boxes around detected objects which allow us to locate and label them in a given scene (or how they move through it).

Exploring Object Object Detection Models: Open-Source vs. Proprietary

There are generally two types of models used in object detection: open-sourced models, such as YOLO (You Only Look Once), and proprietary models, like FLIR's DNN object detection model.

Open-Source Object Detection Models such as YOLO (You Only Look Once)

Open-sourced models, licensed under General Public Licensing (GPL), are available for public and commercial use. They can be utilized across academic, research, or commercial fields. When releasing something under a GPL Licence you agree to allow anyone who reads the source code to copy and distribute your work in any way they want and as such, the GPL License doesn't support the patenting of any software. These models can also resize (down-scales or upscale) images to 640x640 resolution which can result in some information being lost if they aren't the correct aspect ratio. This can influence the performance of this type of model.

FLIR's Proprietary Deep Neural Network (DNN)

At FLIR, we utilize an in-house developed deep neural network (DNN) model designed for real-time object detection. This proprietary model, exclusively created by FLIR, harnesses the power of authentic data spanning decades and precisely controlled synthetic data to optimize software and data collection.

Importantly, this dataset is not publicly available or contaminated, ensuring superior frame-per-second (FPS) performance and exceptionally accurate object detection. Moreover, our network is meticulously crafted to run efficiently on embedded hardware, further enhancing its practicality and versatility.

Seven Advantages of FLIR's DNN Object Detection Model

1. Object Detection and Classification: FLIR's DNN uses neural networks for real-time object detection and classification. This is crucial in traffic scenarios where conditions can change rapidly. It differentiates between image classification and object detection, with the latter involving a computer vision task that involves identifying and locating objects within an image or video frame using bounding boxes to outline each object's position and class. 

2. Customizable Classifications: FLIR's proprietary DNN has the ability to expand and customize new classifications of road users. This is particularly useful in diverse urban environments where non-traditional road users, such as rickshaws and E-scooters, are common. This flexibility allows for more accurate and relevant data collection in different geographical locations.

3. High Quality Data: FLIR's proprietary DNN is designed to align with the resolutions of FLIR's visual and thermal sensors, ensuring no compromise on image or information quality. This results in high-quality data that can be used for more accurate traffic analysis and prediction.

4. Proprietary Technology: As a proprietary model developed by FLIR, users benefit from continuous, controlled development and improvement. This ensures that the model stays at the forefront of technology and continues to provide reliable and accurate traffic detection and data collection.

5. Data Security and Privacy: FLIR's proprietary DNN ensures robust data security and privacy. The data collected is securely stored and processed, and the model focuses on object detection and classification, not individual identification. This ensures valuable traffic data is provided without compromising the privacy of road users.

6. Cyber Security: FLIR follows best practices in cyber security to protect its systems and data. This includes regular security updates and patches, rigorous system testing, and continuous monitoring for potential threats.

7. Controlled Data Access and Compliance: With FLIR's proprietary DNN, data access is strictly controlled, and only authorized personnel can access the data. FLIR ensures that its products, including our proprietary DNN, comply with relevant data protection and privacy regulations.


In conclusion, the method for object classification and detection is of paramount importance in the rapidly progressing world of AI and machine learning.

In comparison to open-source AI models, FLIR's proprietary DNN AI model offers a robust, flexible, and secure solution for traffic detection and data collection, making it a valuable tool in the field of Intelligent Transportation Systems. It provides valuable insights for traffic management while ensuring high standards of data security, privacy, and cybersecurity.

To learn more about our AI-enabled cameras and sensors please visit: Intelligent Transportation Systems - AI | Teledyne FLIR