Improved YOLOv8 Model for Lightweight Pigeon Egg Detection

Animals, Год журнала: 2024, Номер 14(8), С. 1226 - 1226

Опубликована: Апрель 19, 2024

In response to the high breakage rate of pigeon eggs and significant labor costs associated with egg-producing farming, this study proposes an improved YOLOv8-PG (real versus fake egg detection) model based on YOLOv8n. Specifically, Bottleneck in C2f module YOLOv8n backbone network neck are replaced Fasternet-EMA Block Fasternet Block, respectively. The is designed PConv (Partial Convolution) reduce parameter count computational load efficiently. Furthermore, incorporation EMA (Efficient Multi-scale Attention) mechanism helps mitigate interference from complex environments pigeon-egg feature-extraction capabilities. Additionally, Dysample, ultra-lightweight effective upsampler, introduced into further enhance performance lower overhead. Finally, EXPMA (exponential moving average) concept employed optimize SlideLoss propose EMASlideLoss classification loss function, addressing issue imbalanced data samples enhancing model's robustness. experimental results showed that F1-score, mAP50-95, mAP75 increased by 0.76%, 1.56%, 4.45%, respectively, compared baseline model. Moreover, reduced 24.69% 22.89%, Compared detection models such as Faster R-CNN, YOLOv5s, YOLOv7, YOLOv8s, exhibits superior performance. reduction contributes lowering deployment facilitates its implementation mobile robotic platforms.

Язык: Английский

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Real-Time Accurate Apple Detection Based on Improved YOLOv8n in Complex Natural Environments

Plants, Год журнала: 2025, Номер 14(3), С. 365 - 365

Опубликована: Янв. 25, 2025

Efficient and accurate apple detection is crucial for the operation of apple-picking robots. To improve accuracy speed, we propose a lightweight apple-detection model based on YOLOv8n framework. The proposed introduces novel Self-Calibrated Coordinate (SCC) attention module, which enhances feature extraction, especially partially occluded apples, by effectively capturing spatial channel information. Additionally, replace C2f module within neck with Partial Convolution Module improved Reparameterization (PCMR), accelerates detection, reduces redundant computations, minimizes both parameter count memory access during inference. further optimize model, fuse multi-scale features from second third pyramid levels backbone architecture, achieving design suitable real-time detection. address missed detections misclassifications, Polynomial Loss (PolyLoss) integrated, enhancing class discrimination different subcategories. Compared to original YOLOv8n, increases mAP 2.90% 88.90% improves speed 220 FPS, 30.55% faster. it 89.36% FLOPs 2.47%. Experimental results demonstrate that outperforms mainstream object-detection algorithms, including Faster R-CNN, RetinaNet, SSD, RT-DETR-R18, RT-DETR-R34, YOLOv5n, YOLOv6-N, YOLOv7-tiny, YOLOv9-T YOLOv11n, in speed. Notably, has been used develop an Android application deployed iQOO Neo6 SE smartphone, 40 FPS 26.93% improvement over corresponding deployment enabling This study provides valuable reference designing efficient models resource-constrained

Язык: Английский

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IoT and AI-driven solutions for human-wildlife conflict: advancing sustainable agriculture and biodiversity conservation

Smart Agricultural Technology, Год журнала: 2025, Номер unknown, С. 100829 - 100829

Опубликована: Фев. 1, 2025

Язык: Английский

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YOLOv9-LSBN: An improved YOLOv9 model for cotton pest and disease identification method

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Авг. 8, 2024

In order to achieve accurate identification of cotton pests and diseases in a natural complex environment, pest disease detection method based on an improved You Only Look Once version 9(YOLOv9) model was proposed.The RepLanLsk module constructed replaced with RepNCSPELAN4 YOLOv9 enhance the diversity feature extraction establish larger receptive field network; weighted bidirectional pyramid network(BIFPN) added connections between pyramids, ensure optimal fusion, strengthen key features target area.The results showed that had accuracy 93\%, recall rate 92.4\%, average precision 96.4\% for detecting cotton, which were 1.6\%, 0.3\%, 1\% higher than original network model, respectively;Through comparative experiments, it is concluded this YOLOv7, YOLOv8x other models, YOLOv9-LSBN can better extract subtle images, misjudgment lower models. This effectively reduce interference backgrounds, accurately quickly detect targets provide reference crop research backgrounds environments.

Язык: Английский

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Line-Labelling Enhanced Cnns for Transparent Juvenile Fish Crowd Counting

Опубликована: Янв. 1, 2025

Язык: Английский

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A Lightweight Tea Bud-Grading Detection Model for Embedded Applications

Agronomy, Год журнала: 2025, Номер 15(3), С. 582 - 582

Опубликована: Фев. 26, 2025

The conventional hand-picking of tea buds is inefficient and leads to inconsistent quality. Innovations in bud identification automated grading are essential for enhancing industry competitiveness. Key breakthroughs include detection accuracy lightweight model deployment. Traditional image recognition struggles with variable weather conditions, while high-precision models often too bulky mobile applications. This study proposed a YOLOV5 model, which was tested on three types across different scenarios. It incorporated convolutional network compact feature extraction layer, significantly reduced parameter computation. achieved 92.43% precision 87.25% mean average (mAP), weighing only 4.98 MB improving by 6.73% 2.11% reducing parameters 2 141.02 compared YOLOV5n6 YOLOV5l6. Unlike networks that detected single or dual grades, this offered refined advantages both size, making it suitable embedded devices limited resources. Thus, the YOLOV5n6_MobileNetV3 enhanced supported intelligent harvesting research technology.

Язык: Английский

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An improved YOLOv5 model for aeolian saltating particle recognition in high-speed video

Measurement, Год журнала: 2025, Номер unknown, С. 117321 - 117321

Опубликована: Март 1, 2025

Язык: Английский

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Line-Labelling Enhanced CNNs for Transparent Juvenile Fish Crowd Counting

Smart Agricultural Technology, Год журнала: 2025, Номер unknown, С. 100963 - 100963

Опубликована: Апрель 1, 2025

Язык: Английский

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A deep learning-based method for estimating the main stem length of sweet potato seedlings

Measurement, Год журнала: 2024, Номер 238, С. 115388 - 115388

Опубликована: Июль 24, 2024

Язык: Английский

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MLG-YOLO: A Model for Real-Time Accurate Detection and Localization of Winter Jujube in Complex Structured Orchard Environments

Plant Phenomics, Год журнала: 2024, Номер 6

Опубликована: Янв. 1, 2024

Our research focuses on winter jujube trees and is conducted in a greenhouse environment structured orchard to effectively control various growth conditions. The development of robotic system for harvesting crucial achieving mechanized harvesting. Harvesting jujubes efficiently requires accurate detection location. To address this issue, we proposed localization method based the MobileVit-Large selective kernel-GSConv-YOLO (MLG-YOLO) model. First, dataset constructed comprise scenarios lighting conditions leaf obstructions train Subsequently, MLG-YOLO model YOLOv8n proposed, with improvements including incorporation MobileViT reconstruct backbone keep more lightweight. neck enhanced LSKblock capture broader contextual information, lightweight convolutional technology GSConv introduced further improve accuracy. Finally, 3-dimensional combining RGB-D cameras proposed. Through ablation studies, comparative experiments, error tests, full-scale tree tests laboratory environments environments, effectiveness detecting locating confirmed. With MLG-YOLO, mAP increases by 3.50%, while number parameters reduced 61.03% comparison baseline Compared mainstream object models, excels both accuracy size, 92.70%, precision 86.80%, recall 84.50%, size only 2.52 MB. average environmental testing reached 100%, 92.82%. absolute positioning errors

Язык: Английский

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