Guide to Object Detection & Its Applications in 2024

Object detection is what stops your car if you fail to hit the brakes on time.

Apart from making vehicles safer to drive, various other applications of object detection-enabled solutions can be seen in different industries. Many businesses use or plan to use the power of object detection in their digital stack to improve processes and reduce problems within the value chain.

For businesses that are planning to develop and implement object detection, it is important to get a clear understanding of the technology and where it can be best used in your business.

 In this article, we explore what object detection is, how it works, how it differs from image recognition, and what are some of its industry-specific applications.

What is object detection?

Object detection is a computer vision (CV) technique that enables machines to identify and detect objects in digital images or video footage using deep learning and ML. The main goal of object detection is to accurately locate the position and size of one or more objects within a given image/video, along with assigning a class label (explaining what they are) to those objects. This technique involves two key steps: image classification and object localization.

1. Image classification

Image classification is the process of labeling and categorizing an image as a particular object based on predefined rules to explain what it contains. For instance, an image of a dog might be classified as a “dog,” while an image of a bird might be classified as a “bird.” Image classification typically involves using deep learning models, such as convolutional neural networks (CNNs), to analyze and categorize images based on their visual features.

2. Object localization

Object localization involves determining the exact location and size of an object within an image. This is done by drawing a bounding box around the object and assigning a class label to that object to make it easier for machines to spot it.

How does object detection work?

Object detection combines image classification and object localization techniques to analyze and categorize objects within an image or video. The basic process of object detection includes the following steps:

1. Inputting an image or multiple images into an object detection algorithm
2. The algorithm analyzes the provided image(s)
3. The algorithm applies a bounding box around each object to identify the location and assigns a class label to that object using image classification.
4. The previous step informs the algorithm where and what the object of the image is.
5. The algorithm then outputs the location, size, and class label for each object in the image.

There are several different object detection algorithms, including R-CNN, Fast R-CNN, YOLO, and Single Shot MultiBox Detector (SSD). These algorithms use a variety of techniques, including CNNs, Region-based fully convolutional networks, etc., to accurately identify and locate objects within an image.

Object Detection vs. Image Recognition: Understanding the Differences

Object detection is often confused with image recognition, but there are some considerable differences between the two.

1. Image recognition refers to analyzing an entire image and identifying the objects within it, without localizing them or determining their exact position. Object detection, on the other hand, involves both identifying objects and localizing them within an image.
2. Object detection is a more complex task as compared to image recognition. For instance, if you wanted to identify a particular vehicle in an image, you could use image recognition to identify the car as an object within the image. However, you would not be able to determine the exact location of the car within the image. 
3. Object detection would not only identify the car as an object within the image but also detect its position and assign a bounding box around the car.

The image below clarifies the difference further:

Top 5 industry-specific applications of object detection

A wide range of use cases of object detection can be found across different industries, from healthcare to retail to autonomous driving. This section highlights some of the most common applications of object detection.

1. Healthcare

In healthcare, specifically in the radiology sector, object detection can be used to identify and localize tumors and other abnormalities in medical images such as MRIs, CT scans, x-rays, etc. This can help doctors and radiologists make more accurate diagnoses and develop more effective treatment plans.

Watch how object detection and AI can reduce radiologists’ workload:

To learn more about computer vision use cases in the healthcare industry, check out this article.

2. Retail

In retail, object detection can be used to optimize inventory management and store security. Retailers can use object detection to track inventory levels by scanning shelves at stores and identifying when products are low in stock.

See how bots use AI and object detection to scan shelves to analyze inventory levels:

If you can not afford expensive bots, cheaper handheld devices are also available on the market that use object detection and computer vision to count inventory:

See how a system enabled with object detection and AI can be used to improve store security:

Walmart also uses camera-enabled with AI and object detection in 1000 of its stores to reduce shoplifting and improve security:

To learn more about the applications of object detection and computer vision in the retail sector, check out this article.

3. Autonomous Driving

Object detection is the fundamental technology used in developing self-driving systems. In autonomous driving, object detection is used to detect and localize other vehicles, pedestrians, and obstacles on the road. This allows self-driving vehicles to navigate safely on the roads and avoid collisions.

See how an autonomous car is being used in China:

4. Manufacturing

Manufacturing products like cars require the assembly of thousands of components, and keeping track of them can be repetitive and error-prone if done manually. Object detection can be used to scan products on assembly lines. This enables improved quality control and ensures that products are assembled correctly.

See how Tesla uses bots enabled with object detection and AI to automate its vehicle assembly line:

5. Agriculture,

Many agricultural companies are now adopting digital solutions which leverage object detection. Object detection can be used in areas such as:

• Crop monitoring,
• Yield estimation
• Pest detection

This can help farmers take corrective measures before the issue ruins the whole crop. For instance, For instance, a recent study presented an object detection algorithm to identify and monitor tomato plants infected with a bacterial disease called speck. The study used a deep learning algorithm to analyze images of tomato plants captured by a camera mounted on a mobile platform, and the algorithm was able to accurately identify speck-infected plants with an accuracy of up to 99%.

This is how object detection worked on the plant images:

Conclusion

Object detection is a powerful technique enabled by computer vision that allows machines to identify and locate objects in digital images or video footage. As the field of AI continues to evolve, we can expect to see even more innovative applications of object detection in the future.

Further reading

• Image Data Collection: What it is and Best Practices
• Video Data Collection: Challenges & Best Practices

If you need help finding a vendor or have any questions, feel free to contact us: