Object detection is a computer vision technique that combines image classification and object localization. It involves identifying and locating objects within an image or video that has led to many of its contemporary advancements across numerous industries. Computer vision based object detection employs Convolutional Neural Networks (CNNs) and similar techniques to recognize, classify as well as locate objects in an image or video stream. It captures objects in a specific vicinity, and in doing, offers two main functionalities. Firstly, Object Localization, which is involved in accurate demarcation of region occupied and pinpointing the position of objects in the images or video achieved through bounding boxes or pixel-level masks. Secondly, Object Classification, the aspect that involves feature based object wise distinction. This is done by entailing feature extraction, processing and ultimately assigning appropriate class labels based on size, texture, color, shape and other inherent characteristics of objects accurately.
Given its significance, AI based image recognition market, that forms the basis of object detection market is likely to observe a growth surge increasing at a CAGR of 11.76% that is from the current market value of USD 2.28 billion in 2023 to USD 3.97 billion in 2028 [Mordor Intelligence].
Applications of Object Detection
Object detection is being widely used as a common feature in the development of autonomous vehicles. Following are the ways in which object detection is applied by these vehicles in real-time:
- Obstacle Detection: For smooth traversing, it is necessary to identify debris, other vehicles, animals and other obstacles in the path. Object detection helps in identifying these such that these vehicles can make informed decisions related to their direction and speed.
- Traffic Signal Detection: Object detection helps these vehicles to move safely and follow traffic regulations by interpreting traffic signs as well as signals on-the-go and act appropriately.
- Pedestrian Detection: According to a report, one-third of accidents caused per year are due to improper detection of pedestrians and cyclists. Human detection using OpenCV plays a vital role in this case, by detecting vulnerable pedestrians and guides these vehicles to avoid such mishappenings.
- Lane Detection: Traversing in the wrong lane can cause delays, accidents and tensed situations amongst drivers. To avoid this, detection of lane markings allows autonomous vehicles to drive along in their designated lanes.
Object detection in the Healthcare industry has various applications and tremendous future potential, some of these applications are given below:
- Cancer Diagnosis: In modern hospitals and diagnostics centers, object detection is used for detecting and locating tumors. Detection systems can be trained using images obtained from X-rays, CT scans and MRI scans. Radiologists utilize this vital information for determining the possibility and intensity of chemotherapy etc sessions and overall treatment plans.
- Patient Monitoring: Object detection is used widely for elderly care and monitoring patients in intensive care units by tracking their movements, vital signs, discomfort, falls and other critical occurences with the utmost privacy-preserved approach.
- Medical Instruments Tracking: By linking object detection algorithms with databases that are accessible only to hospital staff, one can track movement of drugs and various classes of medical instruments within the hospital premises, and related suspicious activity. It is also quite useful for training medical students so as to avoid leaving any instruments inside patients causing sepsis and other complications.
- Anomaly Detection: Object detection can be employed to observe early identification of abnormalities and related trends in images obtained from scans etc, such as osteoporosis, predictive fractures and stones for faster diagnosis and treatment plans.
Similar to autonomous vehicle industry, object detection now-a-days plays an important role in surveillance and security industry, that can be applicable in smart homes and city planning as well. The following are some of the applications of object detection in this industry:
- Crowd Monitoring: Not only object detection helps in detecting crowd behavior, density and suspicious activity in events, stadiums, but also helps in prevailing border security measures.
- Facial Recognition: As an applied version of computer vision and image processing, this technology is an enhanced version of object detection that uses Haar features for cascade classfication. It is used for tracking suspetcs on criminal databases and identity verification in case of banks, offices, colleges etc.
- Object Tracking: One can track objects of interest such as stolen vehicles, ship containers, individuals etc using this technique across premises and even cities.
- Intrusion Detection: Object detection identifies and alerts concerned authority in case there is breach detected in restricted regions. It can be used for access control over these regions.
Moving on to the various applications of object detection in the Retail industry:
- Customer Insights: Retail stores can benefit big time using this technology, since they can gain insights on customer behavior, their movements and preferences, density around check out counters, number of trolleys etc and optimize store layouts accordingly.
- Inventory and Shelf Management: Computer vision based object detection can be used to track inventory in retail stores, warehouses as well as for shelf displays with ample restock product alerts to staff.
- Theft Detection: With inventory softwares and product databases linked to object detection systems, it is easy to detect if any items are being stolen from the store, least crowded areas, suspicious behavior and regions susceptible to theft can be immediately recognised and theft can be prevented.
- Self-Checkout: These automated check-out kiosks work primarily on object detection that recognizes items to be bought and eases the payment process.
- Agriculture: Object detection is used in agricultural industry for crop disease and pest monitoring, weed control, livestock monitoring and precision agriculture (treatment areas).
- Manufacturing: It is greatly used in quality checks, detection of defects, parts recognition and ensuring adherance to workplace safety measures (PPE, restricted areas).
- Sports: This technology assists in ball tracking, player analysis, audience engagement etc.
- Education: In this industry, it can be used for interactive learning, security, attendance etc.
- Environmental Monitoring: It plays a pivotal role in wildlife conservation, wate quality assessment, air quality monitroing as well as natural disaster management.
How KritiKal Can Help You?
KritiKal Solutions leverages its decades of experience and expert resource pool in delivering image processing services in the USA as well as object detection related solutions such as density analysis, object localization, classification and tracking, data variation handling and much more. It has applied object detection in varied use cascross industries such as Seal Detection, ADAS, Drone surveillance, Integrated Gates OCR, Container Mosaicing, Illegal Sand Mining Detection, Machine Displacement Measurement etc.
Over the years, KritiKal Solutions has assisted numerous Fortune500 companies and SMBs in building cusotmized object detection systems. Experience quality services in just a click. Please call us or mail us at email@example.com to avail our services.
Object detection has gained a lot of attention since the advent of the 21st century. Traditional hand-crafted algorithms form the basis of modern object detection technologies such as YOLO, FPN and such. Although some current major concerns around this technology are related to its robustness, occlusions, OpenCV based real-time object detection and processing capabilities, effect of adverse weather conditions, scalability, accuracy, dataset accessibility and such, it surely has a scope of efficiently widening its applications across various industries such as Autonomous Vehicles, Healthcare, Agriculture, Sports, Manufacturing, Retail etc., and improvising further for the benefit of mankind.
Vinay Kumar currently holds the position of Lead Engineer at KritiKal Solutions. With great extensive experience in the fields of computer vision and deep learning, he has been dedicatedly playing a pivotal role in creating and customizing AI systems. He has successfully led and contributed to various AI-based projects at KritiKal to some major clients.