Object Detection Explained: How AI Identifies and Tracks Objects in Real-Time

Object detection is a powerful computer vision technique that goes beyond image classification by not only identifying objects within an image but also pinpointing their exact locations.

This technology is widely used across industries, from enhancing worker safety in manufacturing by detecting people in hazardous areas to optimizing vehicle service centers by detecting vehicles as they enter the bay in real time. By leveraging deep learning models, object detection enables businesses to automate processes, improve accuracy, and make data-driven decisions with greater efficiency. As AI continues to evolve, object detection is becoming a critical tool for streamlining and automating enterprise operations.

How Object Detection Models Work

Object detection combines two key processes:

• Classification identifies what objects are present (e.g., car, person, tree)
• Localization pinpoints where these objects are located within the image, usually by drawing a bounding box around them.

At a high level, object detection models analyze the pixels in an image to identify patterns that indicate the presence of objects. They look for:

• Colors and color patterns associated with specific objects
• Textures that are characteristic of certain materials or surfaces
• Shapes that correspond to common object outlines
• Edges that define object boundaries
• Contextual information from surrounding areas

Models are trained on datasets of labeled images, learning to recognize these features and associate them with specific object classes. Most object detection models consist of three main components:

• Backbone: The backbone takes raw input data, such as images, and transforms it into a structured format, known as feature maps, that can be effectively utilized by the subsequent parts of the network.
• Neck: The neck often sits between the backbone and the head, further refining and combining feature maps.
• Detection Head: Refines data from the backbone into actionable predictions. For object detection, it focuses on identifying and localizing entire objects.

Output of the Object Detection Model

Imagine you are running a frog detection model. This is what your output may look like:

• ‍Box: corner coordinates of the detection box‍
• Label: object type‍
• Confidence: how certain the model is about its predictions

‍

{
"detectorResults": 
  [
      {
        "box": {
          "xyxy": [
            903,
            893,
            978,
            1078
          ]
        },
   "label": "frog",
   "confidence": 0.8913817286491394 
  ]
}

‍

How the Output is Used

Apply Business Logic

Your business logic may only want to count detections over a certain threshold of confidence, let’s say 0.4. You would layer this into your business logic after getting your model results.

Using Single Inferences

For time-sensitive applications, decisions are made based on a single image’s inference. If confidence scores are too low, fallback values or alternative logic may be applied.

Using Multiple Inferences

When sequential images are available, additional logic can improve accuracy, such as:

• Majority voting – If 3 out of 5 frames classify an object as present, consider it detected.
• Weighted averaging – Confidence scores from multiple images are averaged, with a minimum threshold required for a final decision.

Multiple inferences are ideal for scenarios where precision matters more than speed.

Regions of Interest (ROIs)

In some applications, detections are only relevant within specific areas of an image, known as Regions of Interest (ROIs). ROIs can be used to focus the model on a particular section of the image for processing or integrated into business logic after the model generates inferences.

Common Evaluation Metrics for Object Detection Models

While training the model, you will use different evaluation metrics to assess model accuracy. Having a basic understanding of some key terms and common metrics used can be helpful! We’ll cover some common evaluation metrics and some terms below.

Intersection over Union (IoU)

Intersection over Union (IoU) is a technique used in object detection to determine whether a predicted bounding box is considered a true positive or a false positive. It involves setting a threshold value for the Intersection over Union (IoU) score, which measures the overlap between the predicted bounding box and the ground truth bounding box.

Mean Average Precision (mAP)

mAP is a metric used to evaluate the performance of object detection models in computer vision. It combines precision and recall measurements across different object classes and detection thresholds.

• Precision: The fraction of true positives out of all detected objects.
• Recall: The fraction of true positives out of all actual objects in the image.
• Intersection over Union (IoU): Measures the overlap between predicted and ground truth bounding boxes labeled. Refer to Intersection over Union (IoU).
• Average Precision (AP): AP is a metric used to evaluate the performance of an object detection or classification model. It is calculated for each class separately by computing the area under the precision-recall (PR) curve, which plots precision against recall at different confidence thresholds.

A high mAP score indicates that the model can detect objects with both high precision and recall.

Mean Absolute Error (MAE)

MAE is a metric that measures the average difference between predicted and actual counts in object detection scenarios. It helps you understand how accurately your model can count objects in an image or video.

• Predicted Count: The number of objects estimated by the model for a given image or frame.
• Actual Count: The true number of objects present in the image or frame.
• Absolute Error: The absolute difference between the predicted and actual counts.
• Mean: The average of these absolute errors across all evaluated images or frames.
• Calculation: MAE = (Sum of Absolute Errors) / (Number of Images or Frames)

A low MAE score indicates that the model can accurately count objects with minimal deviation from the true count. MAE is particularly useful in scenarios where the total count is more important than individual object detection or localization, such as crowd counting, inventory management, or traffic monitoring.

Enterprise Applications of Object Detection Models

People Detection for Workplace Safety

In environments like manufacturing plants and construction sites, ensuring that only authorized personnel are in designated areas is crucial for safety and compliance. Object detection enables real-time people tracking, allowing businesses to monitor movement, detect unauthorized access, and enforce safety protocols.

With WorkWatch, companies can automatically count personnel in restricted zones, ensuring proper staffing levels and adherence to safety policies. By integrating real-time people detection, businesses can prevent accidents, reduce liability risks, and improve operational oversight without manual monitoring.

Interested in workplace safety automation? Learn more about our WorkWatch product and its capabilities.

Vehicle Detection for Automotive Service Centers

Object detection plays a key role in vehicle identification and tracking, particularly in automotive service centers where efficiency is essential. By detecting vehicles as they enter service bays, businesses can automate processes like vehicle check-in, service history retrieval, and queue management.

With PitCrew, service centers can instantly recognize vehicle types, retrieve maintenance records, and streamline operations to reduce customer wait times. The integration of vehicle detection helps improve workflow efficiency, enhance customer experience, and increase service throughput.

Interested in automating vehicle identification? Learn more about our PitCrew product and its capabilities.

Queue Analytics for Retail & Hospitality

Object detection enables businesses to analyze customer queues in real time, optimizing staffing and service efficiency. By tracking customer movement and wait times, businesses can adjust resources dynamically to improve throughput and reduce bottlenecks.

With ExpressLane, retailers, QSRs, and service centers gain insights into queue lengths, peak times, and service speed, allowing for data-driven decisions to enhance customer satisfaction. Businesses can proactively manage wait times, optimize staffing levels, and improve service quality.

Interested in real-time queue analytics? Learn more about our ExpressLane product and its capabilities.

Conclusion

Object detection models provide valuable insights by identifying and locating objects in images and video. By understanding how these models work and integrating business logic effectively, you can optimize their use for real-world applications. Whether using single or multiple inferences, setting confidence thresholds, or focusing on ROIs, thoughtful implementation ensures accuracy and efficiency in decision-making.

‍

‍