Object Detection: An Introduction

Spotting relevant information in an image or video feed can feel a little like looking for a needle in a haystack, but it doesn’t have to require so much time and effort. Training AI to detect objects can help by streamlining and automating the process of generating insights from visual data. 

    What Is Object Detection?

    Object detection is a common and important computer vision task that involves identifying and accurately labeling objects within images, video frames, or live feeds. It involves labeling each instance of a specific object within an image. An object detection model will draw bounding boxes around objects, learning to identify and predict future instances as it processes more visual data over time.    

      Object Detection: Real-World Use Cases

      Object detection is an essential part of many common computer vision applications. Models for detecting objects can provide the foundation for projects related to adjacent tasks like instance segmentation and object tracking, offering high-value use cases to businesses in any industry. 

      Precision Counting

      Plainsight empowers businesses to quickly develop and deploy vision AI models capable of counting people, animals, or any relevant objects with a higher level of accuracy than manual methods. Agribusinesses, for example, have used a Precision Livestock Counting model to attain more than 99% accuracy.

      Dangerous Object Identification

      Recalls have an impact far beyond dollar figures, often affecting a company’s reputation years after products are pulled from shelves. With object detection models for identifying anomalies, manufacturers of all types can keep their processes running smoothly and keep customers safe. Proactive, predictive monitoring can draw attention to risk factors early and set off automated alerts.

      PPE Detection

      While organizations in high-risk industries like construction have depended on personal protective equipment forever, people across the world became familiar with the acronym PPE throughout the COVID-19 pandemic. Mask mandates or no mask mandates, object detection models can identify the presence (or absence) of PPE including helmets, reflective gear, footwear, goggles, and over-ear coverings. 

      Defect Detection

      Manufacturers can use object detection models to dependably spot minute defects in their products and set up automated alerts for quick and decisive intervention.  By automating detection and seeing what the human eye may miss, vision AI can help keep consumers safe and help businesses avoid costly recalls. On the secondary marketplace, it can help authenticate and appraise goods

      More Object Detection Use Cases

      • Self-driving cars: Autonomous vehicles represent perhaps the most exciting application for object detection models. These cars rely on object-detecting algorithms to understand their surroundings and recognize pedestrians, signs, and other vehicles.
      • Occupancy counts: Hospitals can keep a real-time occupancy count for areas such as waiting rooms and lobbies to promote efficiency and safety.
      • Drive-thru analysis: For quick service restaurants, introducing object detection models for customer vehicles paves the way for generating insights from drive-thru traffic.

      How Do Object Detection Models Work? 

      Object detection models are most commonly trained through one of the many deep learning methods. Deep learning is a more advanced form of machine learning that makes use of neural networks and typically requires less human involvement than traditional machine learning approaches. Common approaches include: 

      • Region-based Convolutional Neural Network (R-CNN): A three-stage deep learning model, R-CNN breaks images into thousands of “regions of interest” for simpler detection.
      • Fast R-CNN and Faster R-CNN: These approaches address some of the issues with R-CNN to expedite the model training process. Faster R-CNN allows for object detection in near real time.
      • You Only Look Once (YOLO): Even faster than previous approaches, YOLO combines the full processes for object detection and classification into a single neural network. It’s useful for detecting objects in streaming videos. 

      With Plainsight, automated model training with SmartML lowers the barrier to entry so that all types of users can create useful object detection models. Users don’t need to select from – or even have previous experience with – any of these approaches to use the platform.

      Get Started Today

      Start building your own object detection models today by registering for a free account with Plainsight. Interested in learning more about Plainsight’s end-to-end computer vision platform can support your business? Schedule a free demo with our team.

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