The purpose of the research is to perform a detailed comparative analysis of YOLO architectures, from YOLOv3 to YOLOv8, for evaluating their ability to effectively detect people in the water area. The research not only assesses current capabilities, but also suggests directions for future innovation...
Type of disease: Gray mold Imaging Device: Corning microHSI Spectral range: 400-1000nm GPU: NVIDIA RTX3090 X (24 GB memory) Jung et al. [33] Table 2. Hybrid 3D-CNN-based architectures for detection of diseased and defected hyperspectral images of crop. CNN ModelInformationReference Hypernet...
While deep learning models like YOLOv5 have shown promise in real-time object recognition, their practical applicability may be constrained by their high processing requirements. In this paper, we suggest a faster and lighter version of YOLOv5s for wild animal recognition. To lower computational ...
Ding X, Chen H, Zhang X, Huang, K, Han J, Ding G (2022) Re-parameterizing your optimizers rather than architectures. arXiv preprint arXiv:2205.15242 Wang CY, Bochkovskiy A, Liao HYM (2023) YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detector...
This repository offers a comprehensive collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like YOLO11, RT-DETR, SAM 2, Florence-2, PaliG
This paper develops new deep learning methods, namely, deep residual shrinkage networks, to improve the feature learning ability from highly noised vibration signals and achieve a high fault diagnosing accuracy. Soft thresholding is inserted as nonlinear transformation layers into the deep architectures to...
Considering that traditional object detection algorithms have low accuracy in handling PCB images with complex backgrounds, various types, and small-sized defects, this paper proposes a PCB defect detection algorithm based on a novel YOLOv5 multi-scale attention mechanism(EMA) spatial pyramid dilated ...
(CNN) is evolved from the prior knowledge of biological vision systems [72]. The initial development of neural networks fulfills the thrust to simulate human brains. The maturity of these shallow networks (biological vision system) into deep architectures significantly reduces the requirement of ...
In this study, we perform a comprehensive survey of image augmentation for deep learning using a novel informative taxonomy. To examine the basic objective of image augmentation, we introduce challenges in computer vision tasks and vicinity distribution. The algorithms are then classified among three ...
Some examples are YOLO RetinaNet and EfficientDet. SSL Self-Supervised Learning SSVM Smooth support vector machine ST Style Transfer An algorithm that allows to tranfer properties of one object to another (i.e. transfer painitning style to a photography). STDA Style Transfer Data Augmentation ...