Real-Time Garbage Object Detection With Data Augmentation and Feature Fusion Using SUAV Low-Altitude Remote Sensing Images

Chen, Weiyang; Wang, Haifeng; Li, Hao; Li, Quanjing; Yang, Yang; Yang, Kun

doi:10.1109/lgrs.2021.3074415

Cited by 9 publications

(8 citation statements)

References 13 publications

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“…Drones have also contributed to efficient waste management (Chen et al, 2022) and drone monitoring allows for the early detection of fires in tourist areas (Almeida et al, 2017).…”

Section: Cluster Analysis: Trends and Overview Of Drones In Tourism A...mentioning

confidence: 99%

“…Studies presented in this cluster advanced the use of UAVs to efficiently monitor, map and assess the effects of tourism on natural ecosystems, such as forested areas (Runnström et al , 2019), dunes and coastal lagoons (Evelpidou et al , 2021) and species habitat (Schofield et al , 2021; Séguigne et al , 2022). Drones have also contributed to efficient waste management (Chen et al , 2022) and drone monitoring allows for the early detection of fires in tourist areas (Almeida et al , 2017). Cluster 2.…”

Section: Empirical Analysismentioning

confidence: 99%

See 1 more Smart Citation

Drones in hospitality and tourism: a literature review and research agenda

Sevilla

Mendieta-Aragón

Ruiz-Gómez

2023

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Purpose Drones have become an important element within hospitality and tourism. The purpose of this study is to identify the corpus of knowledge and create a research agenda that establishes appropriate guidelines for future study of drone application in hospitality and tourism. Design/methodology/approach This work has been undertaken using a mixed-methods approach that combines quantitative and qualitative research and includes a review of the literature related to the study of drone use in hospitality and tourism. Findings The mixed-methods review identified gaps in the research, potential areas of study to enhance the scientific literature and potential uses of drones in tourism and hospitality for researchers, consumers and industry professionals. Originality/value This study makes an original contribution by establishing an integrated framework, which led to a synthesis of the research corpus and provided a holistic conceptualisation of the relationship between tourism and drones. In addition, the research agenda proposed will help boost and consolidate this emerging field of research.

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“…Drones have also contributed to efficient waste management (Chen et al, 2022) and drone monitoring allows for the early detection of fires in tourist areas (Almeida et al, 2017).…”

Section: Cluster Analysis: Trends and Overview Of Drones In Tourism A...mentioning

confidence: 99%

Section: Empirical Analysismentioning

confidence: 99%

Drones in hospitality and tourism: a literature review and research agenda

Sevilla

Mendieta-Aragón

Ruiz-Gómez

2023

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“…Thirdly, it is proposed to use mosaic method to preprocess the training data, which can improve the detection ability for small-scale targets. What's more, Mish activation function, SAM-block and CIOU were used in YOLOv4, which contribute a lot to the detection accuracy [46]. Recently, YOLOv4 has been widely used in the field of remote sensing and achieved good results [47].…”

Section: Basic Of Yolov4mentioning

confidence: 99%

Light-YOLOv4: An Edge-Device Oriented Target Detection Method for Remote Sensing Images

Xiong

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Most deep-learning based target detection methods have high computational complexity and memory consumption, and they are difficult to be deployed on edge devices with limited computing resources and memory. To tackle this problem, this paper proposes to learn a lightweight detector named Light-YOLOv4, and it is obtained from YOLOv4 through model compression. To this end, firstly, we perform sparsity training by applying L1 regularization to the channel scaling factors, so the less important channels and layers can be found. Secondly, channel pruning and layer pruning are enforced on the network to prune the less important parts, which could significantly reduce network's width and depth. Thirdly, the pruned model is retrained with knowledge distillation method to improve the detection accuracy. Fourthly, the model is quantized from FP32 to FP16, and it could further accelerate the model with almost no loss of detection accuracy. Finally, to evaluate Light-YOLOv4's performance on edge devices, Light-YOLOv4 is deployed on NVIDIA Jetson TX2. Experiments on SAR ship detection dataset (SSDD) demonstrate that the model size, parameter size and FLOPs of Light-YOLOv4 have been reduce by 98.63%, 98.66% and 91.30% compared with YOLOv4, and the detection speed has been increased to 4.2 times. While the detection accuracy of Light-YOLOv4 is only slightly reduced, for example, the mAP has only reduced by 0.013. Besides, experiments on Gaofen Airplane dataset also prove the feasibility of Light-YOLOv4. Moreover, compared with other state-of-the-art methods, such as SSD and FPN, Light-YOLOv4 is more suitable for edge devices.

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“…In line with garbage image classification, ref. [10] uses artificial intelligence to detect garbage with objects detection systems with aerial vehicle devices.…”

Section: Introductionmentioning

confidence: 99%

The Development of Real-Time Mobile Garbage Detection Using Deep Learning

Fathurrahman

Ma’arif

Chin

2022

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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The problem of garbage in the world is a serious issue that must be solved. Good garbage management is a must for now and in the future. Good garbage management is accompanied by a system of classification and sorting of garbage types. This study aims to create a mobile-based application that can select the type of garbage and enter the garbage data into a database. The database used is a Google SpreadSheet that will accommodate data from the output issued by the garbage detection mobile application. The image data used in this study amounted to 10108 images and was divided into six different garbage classes. This study uses a deep learning platform called densenet121 with an accuracy rate of 99.6% to train the image data. DenseNet121 has been modified and added an optimization based on a genetic algorithm. The genetic algorithm applied in the optimization uses four generations. The model resulting from the training of the two approaches is converted into a model that mobile applications can access. The mobile application based on a deep learning model accommodates the detection data of the type of garbage, the level of detection accuracy, and the GPS location of the garbage. In the final experiment of the mobile application, the delay time in sending data was very fast, which was less than one second (0.86s).

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Real-Time Garbage Object Detection With Data Augmentation and Feature Fusion Using SUAV Low-Altitude Remote Sensing Images

Cited by 9 publications

References 13 publications

Drones in hospitality and tourism: a literature review and research agenda

Drones in hospitality and tourism: a literature review and research agenda

Light-YOLOv4: An Edge-Device Oriented Target Detection Method for Remote Sensing Images

The Development of Real-Time Mobile Garbage Detection Using Deep Learning

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