Object detection technology is crucial in AI [artificial intelligence] because it allows the computer to ‘view’ the environment by identifying objects in videos or visual images. Object detection aims to develop a computational model that offers the most basic information like what and where of the objects.
Object detection working
It is performed in two ways –
- Image processing
Image processing is a traditional method that does not need historical data or supervision.
- No need for annotating images [for supervised training data was labeled manually].
- It has limitations like occlusion [partly hidden objects], complex scenarios [no unicolor background], as well as illumination, shadows, and chaotic outcomes.
- Deep learning method
Deep learning is a modern method that depends on supervision training. GPU’s computation power restricts the performance, which is rapidly escalating.
- Object detection with this method is considerably more vigorous to complex scenes, challenging illumination, and occlusion.
- Lots of training data are needed. The image annotation process is expensive and tedious.
Today, the deep learning method for object detection is accepted and adopted widely for building hi-tech products. The modified version of object detection is ‘person detection’, which is used to detect people in video streams like in CCTV surveillance systems. Currently, deep learning algorithms offer sold person detection results. With a data science company Algoscale, businesses can start their AI-driven digital transformation project. Object detection is applied in several sectors and the team from Algoscale can help you with the deep learning method, which currently leads the advanced detection method.
Applications of object detection technology
Object detection technology is used in different ways. It is employed in computer vision programs ranging from productivity analysis to sports production. Currently, object recognition is essential in the majority of vision-based AI programs and software. Object detection plays a huge role in understanding the scenes associated with military, security, medical, and transportation.
Driverless or self-driving cars use object detection to identify traffic signs, pedestrians, other vehicles, etc. For example, Hyundai IONIQ 5 is the latest robotaxi unveiled by Motional. Optimus Ride, Tesla, Waymo, etc. are great autonomous driving cars that use object detection heavily to view the surrounding threats like obstacles or oncoming vehicles.
Retailers can place object detection models smartly – one to track hands and capture what is picked, while another to monitor shelf space. Computer vision can be applied for checkout – rather than scanning one item after another place everything together. The cameras detect and log in every item. AI-based consumer analysis helps to understand customer interaction and experience, store layout optimization, inventory checks, and smooth operations.
CCTVs work on object detection technology, which is used a lot for security reasons. For example, it helps to detect people in dangerous or restricted areas [like on railway tracks with the purpose to commit suicide] or automate inspection tasks at remote locations using computer vision.
In agriculture, object detection helps to perform tasks like animal monitoring, counting, and quality evaluation of agricultural products. With machine learning algorithms damaged produce is identified during processing. Even infected crops are identified, which helps farmers to take measures.
Tracking ball in sports
Football, cricket, volleyball, etc. needs deep analysis and offers multi-dimensional data to the increasing number of sports fans. Different people need a distinct type of data, which increases the information scale and space needed. Keeping track of ball movement is crucial to extract data from sports video sequences.
In industrial processes, manual work is replaced by robots and currently IoT that uses object detection technology. Current AI advancement has accelerated this trend in quality management, assembly line, and sorting.
Object detection is a boon for the medical community. Medical diagnostics depend a lot on scans, images, and photographs. Object detection involves MRI and CT scans that are extremely useful for disease diagnosis.
In a vast event or festival or a mall, object detection is handy because it helps to separate the mass and measure different groups.
Object detection has huge potential to transform the world. The popular object detection algorithms to understand include Yolo, R-CNN, and Mask R-CNN. However, the model is still in the development phase. You can pursue the skill and develop the necessary skills for machine learning.