Improved YOLO Based Detection Algorithm for Floating Debris in Waterway

Lin, Feng; Tian, Hui; Jin, Qiannan; You, Aiju

doi:10.3390/e23091111

Cited by 44 publications

(23 citation statements)

References 35 publications

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“…The performance of YOLOv2-tiny is clearly worse than that of YOLOv2, YOLOv3, and YOLOv3-tiny as small objects tend to be ignored by YOLOv2. This is likely due to the lack of multi-scale feature maps in YOLOv2 [73]. Previous research [59] found that YOLOv2 provides mAP 47.9 with average IoU 54.7 in the plastic detection compared to 0.809 at IoU 0.5 for YOLOv4 pre-trained here.…”

Section: Experiments I Ii Iii and Iv: Plastic Detection In Uav Imagerymentioning

confidence: 73%

Detection of River Plastic Using UAV Sensor Data and Deep Learning

et al. 2022

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Plastic pollution is a critical global issue. Increases in plastic consumption have triggered increased production, which in turn has led to increased plastic disposal. In situ observation of plastic litter is tedious and cumbersome, especially in rural areas and around transboundary rivers. We therefore propose automatic mapping of plastic in rivers using unmanned aerial vehicles (UAVs) and deep learning (DL) models that require modest compute resources. We evaluate the method at two different sites: the Houay Mak Hiao River, a tributary of the Mekong River in Vientiane, Laos, and Khlong Nueng canal in Talad Thai, Khlong Luang, Pathum Thani, Thailand. Detection models in the You Only Look Once (YOLO) family are evaluated in terms of runtime resources and mean average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5. YOLOv5s is found to be the most effective model, with low computational cost and a very high mAP of 0.81 without transfer learning for the Houay Mak Hiao dataset. The performance of all models is improved by transfer learning from Talad Thai to Houay Mak Hiao. Pre-trained YOLOv4 with transfer learning obtains the overall highest accuracy, with a 3.0% increase in mAP to 0.83, compared to the marginal increase of 2% in mAP for pre-trained YOLOv5s. YOLOv3, when trained from scratch, shows the greatest benefit from transfer learning, with an increase in mAP from 0.59 to 0.81 after transfer learning from Talad Thai to Houay Mak Hiao. The pre-trained YOLOv5s model using the Houay Mak Hiao dataset is found to provide the best tradeoff between accuracy and computational complexity, requiring model resources yet providing reliable plastic detection with or without transfer learning. Various stakeholders in the effort to monitor and reduce plastic waste in our waterways can utilize the resulting deep learning approach irrespective of location.

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Section: Experiments I Ii Iii and Iv: Plastic Detection In Uav Imagerymentioning

confidence: 73%

Detection of River Plastic Using UAV Sensor Data and Deep Learning

et al. 2022

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“…The system identified obstacles in front of users through the YOLOv5s network, effectively guiding visually impaired people to walk safely on sidewalks and crosswalks. Aiming at the problem of real-time monitoring of floating waste in the waterway [19], a FMA (feature map attention) layer was added to improve YOLOv5s, and splicing data enhancement technology was used to improve the training of small and medium-sized target detection effects. In the literature [20], the TR-YOLOv5s network and down sampling principle were proposed, and an attention mechanism was introduced to meet the highprecision and high-efficiency identification requirements for underwater targets.…”

Section: Related Workmentioning

confidence: 99%

Detection of Farmland Obstacles Based on an Improved YOLOv5s Algorithm by Using CIoU and Anchor Box Scale Clustering

Xue

Feng

et al. 2022

Sensors

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It is necessary to detect multi-type farmland obstacles in real time and accurately for unmanned agricultural vehicles. An improved YOLOv5s algorithm based on the K-Means clustering algorithm and CIoU Loss function was proposed to improve detection precision and speed up real-time detection. The K-Means clustering algorithm was used in order to generate anchor box scales to accelerate the convergence speed of model training. The CIoU Loss function, combining the three geometric measures of overlap area, center distance and aspect ratio, was adopted to reduce the occurrence of missed and false detection and improve detection precision. The experimental results showed that the inference time of a single image was reduced by 75% with the improved YOLOv5s algorithm; compared with that of the Faster R-CNN algorithm, real-time performance was effectively improved. Furthermore, the mAP value of the improved algorithm was increased by 5.80% compared with that of the original YOLOv5s, which indicates that using the CIoU Loss function had an obvious effect on reducing the missed detection and false detection of the original YOLOv5s. Moreover, the detection of small target obstacles of the improved algorithm was better than that of the Faster R-CNN.

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“…On the one hand, there is cooperation with other methods, such as use YOLOv3 to extract and classify underwater objects and combine it with a deep learning method based on (Long Short Term Memory)LSTM to determine the location of the underwater objects [7]. On the other hand, the YOLO backbone structure is optimized [8], [9], such as replaced the output layer with deformable convolution to improve the detection speed in the backbone network CSPDarknet53_dcn(P) of YOLOv4. And a new feature fusion module was redesigned to improve the detection accuracy of small objects using multiple scale detection layers [10].…”

Section: Introductionmentioning

confidence: 99%

License Plate Recognition System Based on Improved YOLOv5 and GRU

Shi

Zhao

2023

IEEE Access

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Aiming at the problem that the traditional license plate recognition method lacking of accuracy and speed, an end-to-end deep learning model for license plate location and recognition in natural scenarios was proposed. First, we added an improved channel attention mechanism to the down-sampling process of the You only look once(YOLOv5). Additionally, a location information is added in the ones to minimize the information loss from sampling, which can improve the feature extraction ability of the model. Then we reduce the number of parameters on the input side and set only one class in the YOLO layer, which improves the efficiency and accuracy of the detector for locating license plates. Finally, Gated recurrent units(GRU) + Connectionist temporal classification(CTC) was used to build the recognition network to complete the character segmentation-free recognition task of the license plate, significantly shortened the training time and improved the convergence speed and recognition accuracy of the network. The experimental results show that the average recognition precision of the license plate recognition model proposed in this paper reaches 98.98%, which is significantly better than the traditional recognition algorithm, and the recognition effect is good in complex environment with good stability and robustness.

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Improved YOLO Based Detection Algorithm for Floating Debris in Waterway

Cited by 44 publications

References 35 publications

Detection of River Plastic Using UAV Sensor Data and Deep Learning

Detection of River Plastic Using UAV Sensor Data and Deep Learning

Detection of Farmland Obstacles Based on an Improved YOLOv5s Algorithm by Using CIoU and Anchor Box Scale Clustering

License Plate Recognition System Based on Improved YOLOv5 and GRU

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