Object detection is a computer vision technique that involves identifying objects in an image or video. Object detection algorithms often use machine learning and deep learning for more accurate detection. The aim of object detection is to create models for computation that address the key questions required by computer vision applications: “What objects are where?” This object detection technology has attracted massive attention in the last few years. Here, we will discuss the trending machine learning solutions for object detection. Thus, read the article till the end to learn something exciting!
Why Object Detection Matters?
Object detection is helpful in many cases. It can be beneficial in applications like video surveillance or image retrieval systems. Furthermore, advanced driver assistance systems (ADAS) rely heavily on this technology. ADAS helps cars recognize lanes and pedestrians and thereby improves road safety. Nowadays, object detection is beyond the world of IT experts. Many online platforms are available that can be used for object detection.
Trending Machine Learning Solutions For Object Detection
Several deep learning models have made significant achievements in recent times. Here, we will discuss some trending machine learning solutions for object detection.
- EfficientDet
It is a modern deep-learning model that has generated considerable interest due to its accurate and efficient object detection capabilities. EfficientDet has become a potent tool in the field of computer vision, where attaining high accuracy and computational efficiency is essential.
- Faster R-CNN
Faster R-CNN is an innovative deep-learning model. By introducing the idea of region proposal networks (RPNs) for efficient and precise object localization, faster R-CNN completely changed the subject of object detection.
- YOLOv7
The advanced deep learning model You Only Look Once version 7 has made significant progress in object detection. YOLOv7 incorporates an improved backbone network along with an additional set of detection heads, building upon the foundation of the original YOLO architecture. This model has excellent accuracy and low-end device compatibility concerning real-time object detection.
- Mask R-CNN
Mask R-CNN is a flexible tool for various computer vision applications. It was created to replace Faster R-CNN and integrates object detection and instance segmentation into a single framework. It enables the model to produce accurate masks that outline the shapes of objects it has detected, in addition to object detection itself.
