C2PSA-Enhanced YOLOv11 Architecture: A Novel Approach for Small Target Detection in Cotton Disease Diagnosis

• URL: http://arxiv.org/abs/2508.12219v2
• Date: Tue, 19 Aug 2025 00:54:18 GMT
• Title: C2PSA-Enhanced YOLOv11 Architecture: A Novel Approach for Small Target Detection in Cotton Disease Diagnosis
• Authors: Kaiyuan Wang, Jixing Liu, Xiaobo Cai,
• Abstract summary: This study presents a deep learning-based optimization of YOLOv11 for cotton disease detection.<n>The mobile-deployed system enables real-time disease monitoring and precision treatment in agricultural applications.
• Score: 3.092042419611666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study presents a deep learning-based optimization of YOLOv11 for cotton disease detection, developing an intelligent monitoring system. Three key challenges are addressed: (1) low precision in early spot detection (35% leakage rate for sub-5mm2 spots), (2) performance degradation in field conditions (25% accuracy drop), and (3) high error rates (34.7%) in multi-disease scenarios. The proposed solutions include: C2PSA module for enhanced small-target feature extraction; Dynamic category weighting to handle sample imbalance; Improved data augmentation via Mosaic-MixUp scaling. Experimental results on a 4,078-image dataset show: mAP50: 0.820 (+8.0% improvement); mAP50-95: 0.705 (+10.5% improvement); Inference speed: 158 FPS. The mobile-deployed system enables real-time disease monitoring and precision treatment in agricultural applications.

Related papers

• Dynamic Meta-Ensemble Framework for Efficient and Accurate Deep Learning in Plant Leaf Disease Detection on Resource-Constrained Edge Devices [0.0]
  We introduce a novel Dynamic Meta-Enemble Framework (DMEF) for high-accuracy plant disease diagnosis under resource constraints.<n>DMEF employs an adaptive weighting mechanism that dynamically combines the predictions of three lightweight convolutional neural networks.<n>Experiments on benchmark datasets for potato and maize diseases demonstrate state-of-the-art classification accuracies of 99.53% and 96.61%, respectively.
  arXiv  Detail & Related papers  (2026-01-24T03:57:49Z)
• YOLOv11-Litchi: Efficient Litchi Fruit Detection based on UAV-Captured Agricultural Imagery in Complex Orchard Environments [6.862722449907841]
  This paper introduces YOLOv11-Litchi, a lightweight and robust detection model specifically designed for UAV-based litchi detection.<n>YOLOv11-Litchi achieves a parameter size of 6.35 MB - 32.5% smaller than the YOLOv11 baseline.<n>The model achieves a frame rate of 57.2 FPS, meeting real-time detection requirements.
  arXiv  Detail & Related papers  (2025-10-11T09:44:00Z)
• Lightweight Shrimp Disease Detection Research Based on YOLOv8n [0.0]
  Shrimp diseases are one of the primary causes of economic losses in shrimp aquaculture.<n>This paper proposes a lightweight network architecture based on YOLOv8n.
  arXiv  Detail & Related papers  (2025-07-03T06:38:55Z)
• MS-YOLO: A Multi-Scale Model for Accurate and Efficient Blood Cell Detection [4.246758319242548]
  This study proposes the multi-scale YOLO (MS-YOLO), a blood cell detection model based on the YOLOv11 framework.<n>MS-YOLO incorporates three key architectural innovations to enhance detection performance.
  arXiv  Detail & Related papers  (2025-06-04T14:02:24Z)
• Improved YOLOv7 model for insulator defect detection [24.775038970508078]
  This paper proposes an improved YOLOv7 model for multi-type insulator defect detection.<n>The proposed model exhibits enhancements across various performance metrics.
  arXiv  Detail & Related papers  (2025-02-11T02:09:30Z)
• Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation [53.2981100111204]
  Photoplethysmography and electrocardiography can potentially enable continuous blood pressure (BP) monitoring.<n>Yet accurate and robust machine learning (ML) models remains challenging due to variability in data quality and patient-specific factors.<n>In this work, we investigate whether a model pre-trained on one modality can effectively be exploited to improve the accuracy of a different signal type.<n>Our approach achieves near state-of-the-art accuracy for diastolic BP and surpasses by 1.5x the accuracy of prior works for systolic BP.
  arXiv  Detail & Related papers  (2025-02-10T13:33:12Z)
• Efficient Brain Tumor Classification with Lightweight CNN Architecture: A Novel Approach [0.0]
  Brain tumor classification using MRI images is critical in medical diagnostics, where early and accurate detection significantly impacts patient outcomes.<n>Recent advancements in deep learning (DL) have shown promise, but many models struggle with balancing accuracy and computational efficiency.<n>We propose a novel model architecture integrating separable convolutions and squeeze and excitation (SE) blocks, designed to enhance feature extraction while maintaining computational efficiency.
  arXiv  Detail & Related papers  (2025-02-01T21:06:42Z)
• Breast Cancer Histopathology Classification using CBAM-EfficientNetV2 with Transfer Learning [0.0]
  This study introduces a novel approach leveraging EfficientNetV2 models to improve feature extraction and focus on relevant tissue regions.<n>The EfficientNetV2-XL with CBAM achieved outstanding performance, reaching a peak accuracy of 99.01 percent and an F1-score of 98.31 percent at 400X magnification.
  arXiv  Detail & Related papers  (2024-10-29T17:56:05Z)
• MedDet: Generative Adversarial Distillation for Efficient Cervical Disc Herniation Detection [24.833129797776422]
  Cervical disc herniation (CDH) is a prevalent musculoskeletal disorder that requires labor-intensive analysis from experts. Despite advancements in automated detection of medical imaging, two significant challenges hinder the real-time application of these methods. We introduce MedDet, which leverages the multi-teacher single-student knowledge distillation for model compression and efficiency. Lastly, we conduct comprehensive experiments on the CDH-1848 dataset, achieving up to a 5% improvement in mAP compared to previous methods.
  arXiv  Detail & Related papers  (2024-08-30T18:38:19Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans [43.06293430764841]
  This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions.
  arXiv  Detail & Related papers  (2024-07-02T16:44:00Z)
• COVID-19 Detection Using CT Image Based On YOLOv5 Network [31.848436570442704]
  The dataset provided by Kaggle platform and we choose YOLOv5 as our model. We introduce some methods on objective detection in the related work section. The objection detection can be divided into two streams: onestage and two stage.
  arXiv  Detail & Related papers  (2022-01-24T21:50:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.