Soft-NMS-Enabled YOLOv5 with SIOU for Small Water Surface Floater Detection in UAV-Captured Images

Chen, F. Frank; Zhang, Lanxin; Kang, Sanggil; Chen, L. Leon; Dong, Huaimin; Dan, Li; Wu, Xiaozhu

doi:10.3390/su151410751

Cited by 11 publications

(5 citation statements)

References 55 publications

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“…Zhang et al [13] leveraged reparameterization techniques for feature extraction and propose a spatial to depth convolution strategy to enhance the detection of small targets on water surfaces. Chen et al [43] augmented the capabilities of YOLOv5 for the detection of minor floating objects on water surfaces through the application of SIoU and soft NMS. In parallel, another study by Zhang et al [12] integrated multi-level features to refine the faster R-CNN model, thereby amplifying the velocity of water surface detection tasks.…”

Section: Water Surface Object Detectionmentioning

confidence: 99%

Enhanced Water Surface Object Detection with Dynamic Task-Aligned Sample Assignment and Attention Mechanisms

Zhao,

Qiu,

Chen

2024

Sensors

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The detection of objects on water surfaces is a pivotal technology for the perceptual systems of unmanned surface vehicles (USVs). This paper proposes a novel real-time target detection system designed to address the challenges posed by indistinct bottom boundaries and foggy imagery. Our method enhances the YOLOv8s model by incorporating the convolutional block attention module (CBAM) and a self-attention mechanism, examining their impact at various integration points. A dynamic sample assignment strategy was introduced to enhance the precision of our model and accelerate its convergence. To address the challenge of delineating bottom boundaries with clarity, our model employs a two-strategy approach: a threshold filter and a feedforward neural network (FFN) that provides targeted guidance for refining these boundaries. Our model demonstrated exceptional performance, achieving a mean average precision (mAP) of 47.1% on the water surface object dataset, which represents a 1.7% increase over the baseline YOLOv8 model. The dynamic sample assignment strategy contributes a 1.0% improvement on average precision at the intersection over union (IoU) threshold of 0.5 (AP0.5), while the FFN strategy fine-tunes the bottom boundaries and achieves an additional 0.8% improvement in average precision at IoU threshold of 0.75 (AP0.75). Furthermore, ablation studies have validated the versatility of our approach, confirming its potential for integration into various detection frameworks.

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Section: Water Surface Object Detectionmentioning

confidence: 99%

Enhanced Water Surface Object Detection with Dynamic Task-Aligned Sample Assignment and Attention Mechanisms

Zhao,

Qiu,

Chen

2024

Sensors

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“…Jiang et al [50] evaluated the detection accuracy of YOLOv3, YOLOv4, and YOLOv5 series models on infrared thermal images and videos, with YOLOv5s demonstrating the best performance. Chen et al [51,52] compared the detection performance of Faster R-CNN, SSD, and YOLOv5 on a water surface floater test dataset. However, in this study, the evaluation results demonstrate that YOLOv5m outperforms YOLOv5x in terms of detection performance, highlighting that enhancing the network depth and width does not guarantee a proportional improvement in the model's accuracy across different datasets.…”

Section: Model Performancementioning

confidence: 99%

Early-Stage Pine Wilt Disease Detection via Multi-Feature Fusion in UAV Imagery

Xie,

Wang,

Liu

et al. 2024

Forests

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Pine wilt disease (PWD) is a highly contagious and devastating forest disease. The timely detection of pine trees infected with PWD in the early stage is of great significance to effectively control the spread of PWD and protect forest resources. However, in the spatial domain, the features of early-stage PWD are not distinctly evident, leading to numerous missed detections and false positives when directly using spatial-domain images. However, we found that frequency domain information can more clearly express the characteristics of early-stage PWD. In this paper, we propose a detection method based on deep learning for early-stage PWD by comprehensively utilizing the features in the frequency domain and the spatial domain. An attention mechanism is introduced to further enhance the frequency domain features. Employing two deformable convolutions to fuse the features in both domains, we aim to fully capture semantic and spatial information. To substantiate the proposed method, this study employs UAVs to capture images of early-stage pine trees infected with PWD at Dahuofang Experimental Forest in Fushun, Liaoning Province. A dataset of early infected pine trees affected by PWD is curated to facilitate future research on the detection of early-stage infestations in pine trees. The results on the early-stage PWD dataset indicate that, compared to Faster R-CNN, DETR and YOLOv5, the best-performing method improves the average precision (AP) by 17.7%, 6.2% and 6.0%, and the F1 scores by 14.6%, 3.9% and 5.0%, respectively. The study provides technical support for early-stage PWD tree counting and localization in the field in forest areas and lays the foundation for the early control of pine wood nematode disease.

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“…The neck fuses multi-scale features extracted from the backbone network. YOLOv5 adopts a feature pyramid network structure that combines Feature Pyramid Networks (FPN) [29] and Path Aggregation Network (PANet) [30]. It integrates features from different levels through upsampling and downsampling operations to enhance object detection accuracy.…”

Section: Yolov5 Model Algorithmmentioning

confidence: 99%

Research on the Rapid Recognition Method of Electric Bicycles in Elevators Based on Machine Vision

Zhao,

Li,

et al. 2023

Sustainability

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People are gradually coming around to the idea of living a low-carbon lifestyle and using green transportation, and given the severe urban traffic congestion, electric bicycle commuting has taken over as the preferred mode of short-distance transportation for many. Since batteries are used to power electric bicycles, there are no greenhouse gas emissions while they are in use, which is more in line with the requirement for sustainable development around the world. The public has been increasingly concerned about the safety issues brought on by electric bicycles as a result of the industry’s quick development and the rapid increase in the number of electric bicycles worldwide. The unsafe operation of the elevator and the safety of the building have been seriously compromised by the unauthorized admission of electric bicycles into the elevator. To meet the need for fast detection and identification of electric bicycles in elevators, we designed a modified YOLOv5-based identification approach in this study. We propose the use of the EIoU loss function to address the occlusion problem in electric bicycle recognition. By considering the interaction ratio and overlap loss of the target frames, we are able to enhance localization accuracy and reduce the missed detection rate of occluded targets. Additionally, we introduce the CBAM attention mechanism in both the backbone and head of YOLOv5 to improve the expressive power of feature maps. This allows the model to prioritize important regions of the target object, leading to improved detection accuracy. Furthermore, we utilize the CARAFE operator during upsampling instead of the nearest operator in the original model. This enables our model to recover details and side information more accurately, resulting in finer sampling results. The experimental results demonstrate that our improved model achieves an mAP of 86.35 percent, a recall of 81.8 percent, and an accuracy of 88.0 percent. When compared to the original model under the same conditions, our improved YOLOv5 model shows an average detection accuracy increase of 3.49 percent, a recall increase of 5.6 percent, and an accuracy increase of 3.5 percent. Tests in application scenarios demonstrate that after putting the model on the hardware platform Jeston TX2 NX, stable and effective identification of electric bicycles can be accomplished.

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Soft-NMS-Enabled YOLOv5 with SIOU for Small Water Surface Floater Detection in UAV-Captured Images

Cited by 11 publications

References 55 publications

Enhanced Water Surface Object Detection with Dynamic Task-Aligned Sample Assignment and Attention Mechanisms

Enhanced Water Surface Object Detection with Dynamic Task-Aligned Sample Assignment and Attention Mechanisms

Early-Stage Pine Wilt Disease Detection via Multi-Feature Fusion in UAV Imagery

Research on the Rapid Recognition Method of Electric Bicycles in Elevators Based on Machine Vision

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