Table 1_DLML-PC: an automated deep learning and metric learning approach for precise soybean pod classification and counting in intact plants.docx by Yixin Guo (3923948)

“…After 200 epochs, the recognition results of various object detection algorithms were compared to obtain the optimal model. …”

Faster-RCNN. by Junyan Wang (4738518)

“…To address these issues, this paper proposes an improved object detection algorithm named SCI-YOLO11, which optimizes the YOLO11 framework from three aspects: feature extraction, attention mechanism, and loss function. …”

Image 4_DLML-PC: an automated deep learning and metric learning approach for precise soybean pod classification and counting in intact plants.tiff by Yixin Guo (3923948)

“…After 200 epochs, the recognition results of various object detection algorithms were compared to obtain the optimal model. …”

Results of ablation experiments. by Junyan Wang (4738518)

“…To address these issues, this paper proposes an improved object detection algorithm named SCI-YOLO11, which optimizes the YOLO11 framework from three aspects: feature extraction, attention mechanism, and loss function. …”

Structure diagram of SPDConv. by Junyan Wang (4738518)

“…To address these issues, this paper proposes an improved object detection algorithm named SCI-YOLO11, which optimizes the YOLO11 framework from three aspects: feature extraction, attention mechanism, and loss function. …”

Wise-IOU regression diagram. by Junyan Wang (4738518)

“…To address these issues, this paper proposes an improved object detection algorithm named SCI-YOLO11, which optimizes the YOLO11 framework from three aspects: feature extraction, attention mechanism, and loss function. …”

Visualization of detection results. by Junyan Wang (4738518)

“…To address these issues, this paper proposes an improved object detection algorithm named SCI-YOLO11, which optimizes the YOLO11 framework from three aspects: feature extraction, attention mechanism, and loss function. …”

Structure diagram of the SE attention mechanism. by Junyan Wang (4738518)

“…To address these issues, this paper proposes an improved object detection algorithm named SCI-YOLO11, which optimizes the YOLO11 framework from three aspects: feature extraction, attention mechanism, and loss function. …”

Data Sheet 1_Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: a multi-center diagnostic study.docx by Yi Xie (12835)

“…An explainable object detection algorithm was proposed for the identification of fractures. …”

Comprehensive performance comparison with mainstream SOTA models. by Heng Zhao (333503)

“…<p>Evaluation of MSF-DETR against current state-of-the-art object detection algorithms.</p>…”

Table 1_Motion feature extraction using magnocellular-inspired spiking neural networks for drone detection.docx by Jiayi Zheng (3441902)

“…Experimental results in terms of drone detection performance indicate that the incorporation of MG-SNN significantly improves the accuracy of low-altitude drone detection tasks compared to popular small object detection algorithms, acting as a cheap plug-and-play module in detecting small flying targets against complex backgrounds.…”

Open Pit Mine Object Detection Dataset by Lin Gang (18600526)

“…They can utilize this dataset with confidence to train and enhance object detection algorithms that are specifically designed for open-pit mines. …”

A Machine Learning Ecosystem for Filament Detection: Classification, Localization, and Segmentation by Azim Ahmadzadeh (21789602)

“…<p dir="ltr">Over the past decade, object-detection algorithms have achieved remarkable success, but they remain limited when applied to scientific imagery such as telescopic, satellite, and medical images. …”

Training results under different parameters. by Yang Zhang (30734)

“…<div><p>Low-visibility haze environments, marked by their inherent low contrast and high brightness, present a formidable challenge to the precision and robustness of conventional object detection algorithms. This paper introduces an enhanced object detection framework for YOLOv9s tailored for low-visibility haze conditions, capitalizing on the merits of contrastive learning for optimizing local feature details, as well as the benefits of multiscale attention mechanisms and dynamic focusing mechanisms for achieving real-time global quality optimization. …”

Data Sheet 1_Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model.docx by Changjiang Liang (21099887)

“…In addition, YOLOv8-FPDW was more competitive than mainstream object detection algorithms. The study predicted the optimal harvest period for litchis, providing scientific support for orchard batch harvesting and fine management.…”

The efficient multi-scale attention. by Yang Zhang (30734)

“…<div><p>Low-visibility haze environments, marked by their inherent low contrast and high brightness, present a formidable challenge to the precision and robustness of conventional object detection algorithms. This paper introduces an enhanced object detection framework for YOLOv9s tailored for low-visibility haze conditions, capitalizing on the merits of contrastive learning for optimizing local feature details, as well as the benefits of multiscale attention mechanisms and dynamic focusing mechanisms for achieving real-time global quality optimization. …”

The improved network diagram of YOLOv9s. by Yang Zhang (30734)

“…<div><p>Low-visibility haze environments, marked by their inherent low contrast and high brightness, present a formidable challenge to the precision and robustness of conventional object detection algorithms. This paper introduces an enhanced object detection framework for YOLOv9s tailored for low-visibility haze conditions, capitalizing on the merits of contrastive learning for optimizing local feature details, as well as the benefits of multiscale attention mechanisms and dynamic focusing mechanisms for achieving real-time global quality optimization. …”

Model performance with different sparsity rates. by Zhongjian Xie (4633099)

“…The experimental results showed that P-YOLOv5s-GRNF increased the mAP(mean average precision) by 0.8%, 4.3%, 3.2%, 0.7%, 19.3%, 9.8%, 3.1% compared to mainstream object detection algorithms YOLOv5s, YOLOv6s, YOLOv7-tiny, YOLOv8s, YOLOv5s-Shufflenetv2, YOLOv5s-Mobilenetv3, YOLOv5s-Ghost, respectively. …”

Comparison of different lightweight models. by Zhongjian Xie (4633099)

“…The experimental results showed that P-YOLOv5s-GRNF increased the mAP(mean average precision) by 0.8%, 4.3%, 3.2%, 0.7%, 19.3%, 9.8%, 3.1% compared to mainstream object detection algorithms YOLOv5s, YOLOv6s, YOLOv7-tiny, YOLOv8s, YOLOv5s-Shufflenetv2, YOLOv5s-Mobilenetv3, YOLOv5s-Ghost, respectively. …”

DataSheet1_A novel dataset and deep learning object detection benchmark for grapevine pest surveillance.pdf by Giorgio Checola (20418239)

“…Assisted by entomologists, we performed the annotation process, trained, and compared the performance of two state-of-the-art object detection algorithms: YOLOv8 and Faster R-CNN. Pre-processing, including automatic cropping to eliminate irrelevant background information and image enhancements to improve the overall quality of the dataset, was employed. …”