Harness the power of Computer Vision with YOLOv4 to automatically extract content from images. This project uses custom object detection to identify diagrams within images, making …

First, a lightweight feature extraction network GhostNet is adopted as the backbone, and an efficient channel attention mechanism is introduced to enhance feature extraction.

This article discusses about the YOLOv4's architecture. It outperforms the other object detection models in terms of the inference speeds. It is the ideal choice for Real-time object detection, …

Apr 13, 2022 · In this article, we will discuss how the feature extractor and the neck are designed as well as all these Bof and BoS goodies. To improve accuracy, we can design a deeper …

Jan 4, 2024 · We have recently been amazed at the performance of YOLOv4 on custom object detection tasks and have published tutorials on how to train YOLOv4 in Darknet and how to …

Mar 30, 2025 · YOLOv4 is designed for optimal speed and accuracy in object detection. The architecture of YOLOv4 includes CSPDarknet53 as the backbone, PANet as the neck, and …

Feb 8, 2025 · YOLOv4 (You Only Look Once) is a real-time object detection algorithm that uses a single neural network to predict bounding boxes and class probabilities for each object in an …

Jun 10, 2024 · Feature extraction is a critical step in image processing and computer vision, involving the identification and representation of distinctive structures within an image. This …

Four apparent blocks for YOLOv4 are backbone, neck, dense prediction, and sparse prediction (mainly used in faster R-CNN). The backbone used in YOLOv4 is CSP, which is abbreviated …

Oct 4, 2022 · YOLOv4 framework works in a series of steps. The first step of the algorithm is taking the input image. The next step is dividing the image into grid form, which can be any m …