Cherry detection algorithm based on improved YOLOv5s network

Gai, Rongli; Li, Mengke; Chen, Na

doi:10.1109/hpcc-dss-smartcity-dependsys53884.2021.00312

Cited by 10 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Qiao et al [6] introduced ShuffleNetv2 network proposed by Ma et al [25] into YOLOv5 for a counting method of red jujube, which noted AP of 94% and speed of 35.5 fps. Gai et al [26] documented a cherry detection of 0.08 and 0.03 for F 1 -score higher than the YOLOv4 and YOLOv5, respectively, with the proposed YOLOv5s-cherry. Lawal et al [27] used feature concatenation with coordinate attention mechanism (CAM) (Hou et al [28]) to improved YOLOv5s for fruit detection.…”

Section: Related Workmentioning

confidence: 94%

A simplified network topology for fruit detection, counting and mobile-phone deployment

Lawal,

Zhu,

Cheng

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

The complex network topology, deployment unfriendliness, computation cost, and large parameters, including the natural changeable environment are challenges faced by fruit detection. Thus, a Simplified network topology for fruit detection, tracking and counting was designed to solve these problems. The network used common networks of Conv, Maxpool, feature concatenation and SPPF as new backbone and a modified decoupled head of YOLOv8 as head network. At the same time, it was validated on a dataset of images encompassing strawberry, jujube, and cherry fruits. Having compared to YOLO-mainstream variants, the params of Simplified network is 32.6%, 127%, and 50.0% lower than YOLOv5n, YOLOv7-tiny, and YOLOv8n, respectively. The results of mAP@50% tested using test-set show that the 82.4% of Simplified network is 0.4%, -0.2%, and 0.2% respectively more accurate than 82.0% of YOLOv5n, 82.6% of YOLOv7-tiny, and 82.2% of YOLOv8n. Furthermore, the Simplified network is 12.8%, 17.8%, and 11.8% respectively faster than YOLOv5n, YOLOv7-tiny, and YOLOv8n, including outperforming in tracking, counting, and mobile-phone deployment process. Hence, the Simplified network is robust, fast, accurate, easy-to-understand, fewer in parameters and deployable friendly.

show abstract

Section: Related Workmentioning

confidence: 94%

A simplified network topology for fruit detection, counting and mobile-phone deployment

Lawal,

Zhu,

Cheng

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Backbone: A backbone network is usually some classifier network with excellent performance. This module is used to extract some general feature representations [67];…”

Section: B 32 Yolov5 Frameworkmentioning

confidence: 99%

Personal Protective Equipment Detection for Construction Workers: A Novel Dataset and Enhanced YOLOv5 Approach

Yipeng,

Junwu

2024

IEEE Access

View full text Add to dashboard Cite

Current research on personal protective equipment (PPE) detection has mainly focused on hard hats, overlooking the detection of reflective clothing. Therefore, this study aims to address this research gap comprehensively. We achieve this by creating a novel dataset using semi-automatic labeling techniques and enhancing the YOLOv5 model. The dataset consists of four categories, assessing the presence of hard hats and reflective clothing. Additionally, we introduce an attention mechanism and an improved loss function to tackle challenges related to detecting reflective clothing and overlapping detection frames. Through extensive multi-model comparison experiments, our improved model, AL-YOLOv5, outperforms the baseline model with remarkable advancements of 0.9 AP in the data-limited category and 0.4 mAP overall. Notably, our improved model shows substantial progress in detecting reflective clothing, significantly reducing false detections, and improving overlapping bounding frames. In conclusion, this study contributes to PPE detection accuracy through a novel dataset and an improved model.

show abstract

“…Since 2016, this method has received considerable attention, leading to multiple improvements. Upgraded versions include YOLOv5 [14], YOLOv8 [15], and YOLOv9 [16]. The latest algorithm in the YOLO series has been updated to v9, but its fundamental framework remains rooted in the core principles of the YOLO series.…”

Section: Introduction To the Yolov8 Algorithmmentioning

confidence: 99%

Research on Rapid Recognition of Moving Small Targets by Robotic Arms Based on Attention Mechanisms

Cao,

Jiang,

Shen

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

For small target objects on fast-moving conveyor belts, traditional vision detection algorithms equipped with conventional robotic arms struggle to capture the long and short-range pixel dependencies crucial for accurate detection. This leads to high miss rates and low precision. In this study, we integrate the traditional EMA (efficient multi-scale attention) algorithm with the c2f (channel-to-pixel) module from the original YOLOv8, alongside a Faster-Net module designed based on partial convolution concepts. This fusion results in the Faster-EMA-Net module, which greatly enhances the ability of the algorithm and robotic technologies to extract pixel dependencies for small targets, and improves perception of dynamic small target objects. Furthermore, by incorporating a small target semantic information enhancement layer into the multiscale feature fusion network, we aim to extract more expressive features for small targets, thereby boosting detection accuracy. We also address issues with training time and subpar performance on small targets in the original YOLOv8 algorithm by improving the loss function. Through experiments, we demonstrate that our attention-based visual detection algorithm effectively enhances accuracy and recall rates for fast-moving small targets, meeting the demands of real industrial scenarios. Our approach to target detection using industrial robotic arms is both practical and cutting-edge.

show abstract

Cherry detection algorithm based on improved YOLOv5s network

Cited by 10 publications

References 30 publications

A simplified network topology for fruit detection, counting and mobile-phone deployment

A simplified network topology for fruit detection, counting and mobile-phone deployment

Personal Protective Equipment Detection for Construction Workers: A Novel Dataset and Enhanced YOLOv5 Approach

Research on Rapid Recognition of Moving Small Targets by Robotic Arms Based on Attention Mechanisms

Contact Info

Product

Resources

About