Object detection is an advanced form of image classification where a neural network predicts objects in an image and points them out in the form of bounding boxes. Object detection thus refers to the detection and localization of objects in an image that belong to a predefined set of classes. Tasks like detection, recognition, or localization find widespread applicability in real-world scenarios, making object detection (also referred to as object recognition) a very important subdomain of Computer Vision. After reading this article, you'll understand the following: Popular two-step algorithms like Fast-RCNN and Faster-RCNN typically use a Region Proposal Network that proposes regions of interest that might contain objects. The output from the RPN is then fed to a classifier that classifies the regions into classes.
Apart from architectural modifications, there are several other improvements. Go through the YOLO series for detailed information. YOLOv7 improves speed and accuracy by introducing several architectural reforms. Similar to Scaled YOLOv4, YOLOv7 backbones do not use ImageNet pre-trained backbones. Rather, the models are trained using the COCO dataset entirely. The similarity can be expected because YOLOv7 is written by the same authors of Scaled YOLOv4.
Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph. It is a challenging problem that involves building upon methods for object recognition (e.g. In recent years, deep learning techniques are achieving state-of-the-art results for object detection, such as on standard benchmark datasets and in computer vision competitions. Notable is the "You Only Look Once," or YOLO, family of Convolutional Neural Networks that achieve near state-of-the-art results with a single end-to-end model that can perform object detection in real-time. In this tutorial, you will discover how to develop a YOLOv3 model for object detection on new photographs.
The quantification of positively buoyant marine plastic debris is critical to understanding how concentrations of trash from across the world's ocean and identifying high concentration garbage hotspots in dire need of trash removal. Currently, the most common monitoring method to quantify floating plastic requires the use of a manta trawl. Techniques requiring manta trawls (or similar surface collection devices) utilize physical removal of marine plastic debris as the first step and then analyze collected samples as a second step. The need for physical removal before analysis incurs high costs and requires intensive labor preventing scalable deployment of a real-time marine plastic monitoring service across the entirety of Earth's ocean bodies. Without better monitoring and sampling methods, the total impact of plastic pollution on the environment as a whole, and details of impact within specific oceanic regions, will remain unknown. This study presents a highly scalable workflow that utilizes images captured within the epipelagic layer of the ocean as an input. It produces real-time quantification of marine plastic debris for accurate quantification and physical removal. The workflow includes creating and preprocessing a domain-specific dataset, building an object detection model utilizing a deep neural network, and evaluating the model's performance. YOLOv5-S was the best performing model, which operates at a Mean Average Precision (mAP) of 0.851 and an F1-Score of 0.89 while maintaining near-real-time speed.