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17 pages, 66369 KiB

Open AccessArticle

A Banana Ripeness Detection Model Based on Improved YOLOv9c Multifactor Complex Scenarios

by Ge Wang, Yuteng Gao, Fangqian Xu, Wenjie Sang, Yue Han and Qiang Liu

Symmetry 2025, 17(2), 231; https://doi.org/10.3390/sym17020231 - 5 Feb 2025

Cited by 2 | Viewed by 2030

With the advancement of machine vision technology, deep learning and image recognition have become research hotspots in the non-destructive testing of agricultural products. Moreover, using machine vision technology to identify different ripeness stages of fruits is increasingly gaining widespread attention. During the ripening process, bananas undergo significant appearance and nutrient content changes, often leading to damage and food waste. Furthermore, the transportation and sale of bananas are subject to time-related factors that can cause spoilage, necessitating that staff accurately assess the ripeness of bananas to mitigate unwarranted economic losses for farmers and the market. Considering the complexity and diversity of testing environments, the detection model should account for factors such as strong and weak lighting, image symmetry (since there will be symmetrical banana images from different angles in real scenes to ensure model stability), and other factors, while also eliminating noise interference present in the image itself. To address these challenges, we propose methods to improve banana ripeness detection accuracy under complex environmental conditions. Experimental results demonstrate that the improved ESD-YOLOv9 model achieves high accuracy in these conditions. Full article

(This article belongs to the Section Computer)

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13 pages, 4454 KiB

Open AccessArticle

A High-Precision Fall Detection Model Based on Dynamic Convolution in Complex Scenes

by Yong Qin, Wuqing Miao and Chen Qian

Electronics 2024, 13(6), 1141; https://doi.org/10.3390/electronics13061141 - 20 Mar 2024

Cited by 10 | Viewed by 2526

Abstract

Falls can cause significant harm, and even death, to elderly individuals. Therefore, it is crucial to have a highly accurate fall detection model that can promptly detect and respond to changes in posture. The YOLOv8 model may not effectively address the challenges posed by deformation, different scale targets, and occlusion in complex scenes during human falls. This paper presented ESD-YOLO, a new high-precision fall detection model based on dynamic convolution that improves upon the YOLOv8 model. The C2f module in the backbone network was replaced with the C2Dv3 module to enhance the network’s ability to capture complex details and deformations. The Neck section used the DyHead block to unify multiple attentional operations, enhancing the detection accuracy of targets at different scales and improving performance in cases of occlusion. Additionally, the algorithm proposed in this paper utilized the loss function EASlideloss to increase the model’s focus on hard samples and solve the problem of sample imbalance. The experimental results demonstrated a 1.9% increase in precision, a 4.1% increase in recall, a 4.3% increase in mAP0.5, and a 2.8% increase in mAP0.5:0.95 compared to YOLOv8. Specifically, it has significantly improved the precision of human fall detection in complex scenes. Full article

(This article belongs to the Special Issue Advances in Computer Vision and Deep Learning and Its Applications)

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20 pages, 4890 KiB

Open AccessArticle

ESD-YOLOv5: A Full-Surface Defect Detection Network for Bearing Collars

by Jiale Li, Haipeng Pan and Junfeng Li

Electronics 2023, 12(16), 3446; https://doi.org/10.3390/electronics12163446 - 15 Aug 2023

Cited by 7 | Viewed by 2157

Abstract

To address the different forms and sizes of bearing collar surface defects, uneven distribution of defect positions, and complex backgrounds, we propose ESD-YOLOv5, an improved algorithm for bearing collar full-surface defect detection. First, a hybrid attention module, ECCA, was constructed by combining an efficient channel attention (ECA) mechanism and a coordinate attention (CA) mechanism, which was introduced into the YOLOv5 backbone network to enhance the localization ability of object features by the network. Second, the original neck was replaced by the constructed Slim-neck, which reduces the model’s parameters and computational complexity without sacrificing accuracy for object detection. Furthermore, the original head was replaced by the decoupled head from YOLOX, which separates the classification and regression tasks for object detection. Last, we constructed a dataset of defective bearing collars using images collected from industrial sites and conducted extensive experiments. The results demonstrate that our proposed ESD-YOLOv5 detection model achieved an mAP of 98.6% on our self-built dataset, which is a 2.3% improvement over the YOLOv5 base model. Moreover, it outperformed mainstream one-stage object detection algorithms. Additionally, the bearing collar surface defect detection system developed based on our proposed method has been successfully applied in the industrial domain for bearing collar inspection. Full article

(This article belongs to the Special Issue Advances in Computer Vision and Deep Learning and Its Applications)

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