Over the past few years, there has been a rapid growth in deep learning-based algorithms for object detection. These algorithms can be broadly classified into two categories: two-stage detectors, such as Faster R-CNN, and one-stage detectors, such as YOLO.
23 hours ago
15 hours ago · The YOLO algorithm is currently one of the most widely used algorithms in UAV object detection, offering notable advantages in computational speed. YOLOv4 [21] ...
24 hours ago · One-stage algorithms directly compute and predict both the classification and location information of the targets, offering high real-time performance. Typical ...
7 hours ago · two-stage object detection algorithms offer higher detection accuracy, they have lower real-time performance. On the other hand, one-stage object detection ...
11 hours ago · YOLO, one of the most popular object detection algorithms, is known for its speed and accuracy in identifying objects in real-time. It works by dividing an ...
16 hours ago · Considering that traditional object detection algorithms have low accuracy in handling PCB images with complex backgrounds, various types, and small-sized
22 hours ago · Researchers commonly employ one-stage and two-stage methods for tiny object detection. One-stage methods, like SSD (single shot detector) and YOLO (You Only ...
7 hours ago · In this study, a novel method combining contour analysis with deep CNN is applied for fire detection. The method was made for fire detection using two main ...
6 hours ago · YOLOv5 offers real-time predictions, high accuracy, and efficient single forward propagation for detecting multilingual scene text, making it superior in speed ...
11 hours ago · An artificial intelligence-based system capable of detecting potholes, speed bumps and manhole covers, among other road hazards.