Results 81 to 90 of about 5,295 (214)

Real-time Strawberry Detection Based on Improved YOLOv5s Architecture for Robotic Harvesting in open-field environment

, 2023
This study proposed a YOLOv5-based custom object detection model to detect strawberries in an outdoor environment. The original architecture of the YOLOv5s was modified by replacing the C3 module with the C2f module in the backbone network, which ...
He, Zixuan, Karkee, Manoj, Khana, Salik Ram, Zhang, Qin, Zhang, Xin   +4 more
core  

Improving YOLOv5s Algorithm for Detecting Flame and Smoke

IEEE Access
Object detection methods can be used to detect flames and smoke from images or videos for the identification and exploration of fire. In this paper, an improved YOLOv5s algorithm, called GAM-ASFF-YOLOv5s is proposed, which introduces an attention ...
Li Deng, Jin Zhou, Quanyi Liu
doaj   +1 more source

Parts Surface Defect Detection Algorithm Based on Improved YOLOv8s

Engineering Reports, Volume 8, Issue 1, January 2026.
Enhanced YOLOv8s for real‐time surface defect detection: An improved YOLOv8s deep learning model is developed for detecting fatigue and linear cracks on part surfaces. Trained on a high‐resolution crack dataset with diverse environmental and texture conditions, the model achieves mAP@0.5 = 0.9626, mAP@0.5:0.95 = 0.7803, Precision = 0.926, and Recall ...
Zhe Sun, Caiying Qiao, Dongrui Li, Enhua Zhang   +3 more
wiley   +1 more source

Digital Waste Management - detection technology [PDF]

, 2021
In this thesis, the goal has been to improve the data flow from the garbage trucks in Halden municipality. We begin by designing, building, and installing a data capture unit to collect images of the collected waste.
Holmesland, Christoffer René Haaland
core  

Detection and recognition of unsafe behaviors of underground coal miners based on deep learning

Gong-kuang zidonghua
To address challenges such as multi-scale variations in underground targets, occlusion of moving objects, and the excessive similarity between targets and the environment, a deep learning-based method was proposed for detecting and recognizing unsafe ...
GUO Xiaoyuan, ZHU Meiqiang, TIAN Jun, ZHU Beibei   +3 more
doaj   +1 more source

PO-YOLOv5: A defect detection model for solenoid connector based on YOLOv5

PLOS ONE
Solenoid connectors play important role in electronic stability system design, with the features of small size, low cost, fast response time and high reliability. The main production process challenge for solenoid connectors is the accurate detection of defects, which is closely related to safe driving.
Ming Chen, Yuqing Liu, Xing Wei, Zichen Zhang, Oleg Gaidai, Hengshou Sui, Bin Li   +6 more
openaire   +4 more sources

MFMAN-YOLO: A Method for Detecting Pole-like Obstacles in Complex Environment

, 2023
In real-world traffic, there are various uncertainties and complexities in road and weather conditions. To solve the problem that the feature information of pole-like obstacles in complex environments is easily lost, resulting in low detection accuracy ...
Cai, Lei, Liu, Boyu, Wang, Hao, Wang, Yongqiang, Zhou, Congling   +4 more
core  

A Robust Multi‐Oriented License Plate Detector and A Derived End‐to‐End License Plate Recognizer

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This is a research paper on license plate detection and recognition. A new center‐aware license plate detection and end‐to‐end license plate recognition framework is proposed for robust and efficient license plate detection and recognition under unconstrained scenarios.
Xudong Fan, Wei Zhao
wiley   +1 more source

ARTIFICIAL INTELLIGENCE BASED HELIPAD DETECTION WITH CONVOLUTIONAL NEURAL NETWORK [PDF]

When a malfunction occurs in the helicopter or the pilot faints during a flight or performing a duty, and in order to ensure the safety of the pilot and the helicopter, a system must be available to detect the helicopter landing pads, so that the ...
Ahmed Mohammed, Emad, J. Ali, Ahmed, Mohammed Abdullah, Abdullah   +2 more
core   +2 more sources

AHD‐YOLO: An Adaptive Hybrid Dynamic Network for Building Damage Detection

IET Image Processing, Volume 20, Issue 1, January/December 2026.
To address the issues of limited detection accuracy and high computational resource consumption in current deep learning‐based building damage detection, we propose a novel framework, AHD YOLO, built upon YOLOv11. AHD YOLO achieves an optimal balance between detection performance and computational resource efficiency, demonstrating strong potential for
Min Li, Tao Xu, Yinping Jiang, Peiyong Ji, Jingqi Hu, Wenlong Lui, Xuejian Ji, Ruiqiang Guo   +7 more
wiley   +1 more source