Planning to use object detection? 3 things you should know about it
Object detection is a crucial technology in various fields, including digital twins, robotics, and artificial intelligence. It can be utilized for indenting and localizing objects in images, videos, lidar, and point cloud data, for example. However, its use requires a lot of training, in which human labor is usually unavoidable. That is why we at Elomatic are developing automated tools and processes that revolutionize the use of data for object detection.
Object detection is a prominent application of deep learning, which mimics the structure and function of the human brain’s neural networks using artificial neural networks with multiple layers to extract high-level features and make complex decisions. It offers many advantages such as improved accuracy, real-time processing, flexibility and scalability.
Object detection plays a key role in various sectors, for example:
- Autonomous vehicles rely on it for safe navigation.
- Surveillance systems use it for security and anomaly detection.
- In medical field, it aids in precise imaging and diagnosis.
- Retailers leverage it for accurate inventory management and customer analytics.
- Manufacturing quality control also benefits from it by identifying defects and improving production efficiency.
However, if you plan to use object detection, there are a few things you should be aware of.
1. Object detection and image classification are not synonyms
Object detection draws bounding around detected objects and tells what is inside the boxes, whereas image classification only tells what is in the image. While bounding boxes are common in object detection tasks, they are just one way to indicate the presence and location of an object.
In addition to bounding boxes, segmentation techniques provide more nuanced information. Other techniques include semantic segmentation, instance segmentation, and panoptic segmentation.
2. A lot of training is required when using object detection
The magic behind object detection lies in convolutional neural networks (CNNs). They are deep learning algorithms designed for processing structured grid data like images, applying filters to detect patterns, and using pooling layers to reduce dimensionality, enhancing feature detection. To use and fully utilize the CNNs, they must be trained again and again until adequate confidence is achieved.
In the training, data is the most essential factor in the overall success. What is more, you need accurate data annotation for training models to recognize and understand patterns, enabling them to make accurate predictions and classifications. In practice, you have to label or tag data with relevant information to train machine learning models. You also need to add annotations such as bounding boxes, segmentation masks, or class labels to images, videos, point clouds, or text.
To solve these challenges, we at Elomatic are developing automated tools and processes that change how data is used for object detection. This means that companies can start to use object detection more easily and in wider applications.
3. You might need the help of artificial intelligence
Typically, training data preparation and data labeling involve human-intensive work, as creating the right annotations for the training data requires human expertise and meticulous effort. Moreover, these tools are expensive and do not eliminate or minimize the need for manual labeling – raising the question of whether you want to expose your data to an AI cloud service.
The most significant drawback of these tools is that they can’t leverage the engineering data produced by design teams and technical analysis departments to create new applications. When 3D models and technical analyses are produced with Elomatic’s automated tools, it is possible to utilize these in new ways and bring ideas faster to production.
Deep leaning (DL) is a subfield of machine learning (ML) that is a subset of artificial intelligence. Machine learning focuses on algorithms and statistical models enabling computers to learn and make predictions from data without explicit programming. It is widely used in various applications, including image recognition, natural language processing, and recommendation systems.
Semantic Segmentation is a technique that identifies each pixel as belonging to a particular class or object but doesn’t distinguish between different instances of the same class. For instance, in an image with multiple cars, all car pixels are labeled the same, without indicating which pixels belong to which specific car.
Instance Segmentation not only classifies each pixel but also differentiates between individual instances of the same class. Using the same example, instance segmentation would separate Car A from Car B even though they are both cars.
Panoptic Segmentation is a hybrid that provides both semantic and instance segmentation information. This method is crucial in complex applications like autonomous driving, where it identifies and distinguishes between vehicles for safe navigation. Similarly, in medical imaging, it helps not only in detecting cancerous tissue but also in isolating individual tumors. Its dual functionality makes it a go-to choice for scenarios requiring detailed visual understanding.