FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters

Cheng, Yuwei; Zhu, Jiatong; Jiang, Mengxin; Fu, Jie; Pang, Changsong; Wang, Peidong; Sankaran, Kris; Onabola, Olawale; Liu, Yimin; Liu, Dianbo; Bengio, Yoshua

doi:10.1109/iccv48922.2021.01077

Cited by 58 publications

(22 citation statements)

References 36 publications

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“…By exploiting the multi-scale information obtained from the FPN, our proposed method significantly improves the detection performance for heavily occluded objects and small targets. Extensive evaluations have been conducted on two public datasets, the VoD dataset [9] and the Flow dataset [10]. The experimental results demonstrate the superiority of our method compared to the state-of-the-art baseline methods on object detection in urban driving environments.…”

Section: Introductionmentioning

confidence: 93%

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Deep LiDAR-Radar-Visual Fusion for Object Detection in Urban Environments

Xiao,

Liu,

Luan

et al. 2023

Remote Sensing

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Robust environmental sensing and accurate object detection are crucial in enabling autonomous driving in urban environments. To achieve this goal, autonomous mobile systems commonly integrate multiple sensor modalities onboard, aiming to enhance accuracy and robustness. In this article, we focus on achieving accurate 2D object detection in urban autonomous driving scenarios. Considering the occlusion issues of using a single sensor from a single viewpoint, as well as the limitations of current vision-based approaches in bad weather conditions, we propose a novel multi-modal sensor fusion network called LRVFNet. This network effectively combines data from LiDAR, mmWave radar, and visual sensors through a deep multi-scale attention-based architecture. LRVFNet comprises three modules: a backbone responsible for generating distinct features from various sensor modalities, a feature fusion module utilizing the attention mechanism to fuse multi-modal features, and a pyramid module for object reasoning at different scales. By effectively fusing complementary information from multi-modal sensory data, LRVFNet enhances accuracy and robustness in 2D object detection. Extensive evaluations have been conducted on the public VOD dataset and the Flow dataset. The experimental results demonstrate the superior performance of our proposed LRVFNet compared to state-of-the-art baseline methods.

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Section: Introductionmentioning

confidence: 93%

“…We evaluate our LRVFNet on the public VOD dataset [9] and Flow dataset [10] collected in real-world urban environments. The VOD dataset [9] consists of a 64-beam LiDAR, binocular cameras, and a 4D mmWave radar, containing a total of 8693 synchronized and calibrated frames in a challenging urban traffic environment.…”

Section: Datasetmentioning

confidence: 99%

Deep LiDAR-Radar-Visual Fusion for Object Detection in Urban Environments

Xiao,

Liu,

Luan

et al. 2023

Remote Sensing

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“…A similar attention mechanism was also used in [194]. Chen et al [195] proposed a global attention module to adaptively fuse multi-modal features extracted from image and radar data for small floating waste detection [196].…”

Section: Feature Learningmentioning

confidence: 99%

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

Rekavandi¹,

Liu²,

Boussaid³

et al. 2022

Preprint

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Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. Typically, small objects appear in real-world due to large camera-object distance. Because small objects occupy only a small area in the input image (e.g., less than 10%), the information extracted from such a small area is not always rich enough to support decision making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of SOD deep learning based methods. In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research. We investigate how to improve the performance of small object detection in maritime environments, where increasing performance is critical. By establishing a connection between generic and maritime SOD research, future directions have been identified. In addition, the popular datasets that have been used for SOD for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided.

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“…Multi-spectral imagery has also been successfully used for plastic detection in the satellite case (e.g. for plastic detection on seas and oceans (Biermann et al, 2020, Themistocleous et al, 2020), and, more recently, on the rivers thanks to UAV-imagery (Cortesi et al, 2022, Cortesi et al, 2021, Gonc ¸alves and Andriolo, 2022, Maharjan et al, 2022, Cheng et al, 2021.…”

Section: Introductionmentioning

confidence: 99%

Mini Uav-Based Litter Detection on River Banks

Cortesi

Mugnai²,

Angelini³

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Most of the anthropic pollution arriving to seas and oceans is carried by rivers, leading to a dramatic impact on the aquatic flora and fauna. Hence, several strategies have already been considered to reduce the use of plastic (and non biodegradable) objects, fostering recycling, detect litter in the environment, and catch it. This work aims at implementing a litter detection approach usable also in urban areas, hence considering a mini-UAV (Unmanned Aerial Vehicle) in order to reduce the issues related to flight restrictions. The implemented strategy exploits a high resolution map of the area of interest, a properly trained deep learning litter object detector, and a vision based localization system, which allows to remarkably reduce the positioning error of the UAV navigation system, in order to provide estimates of the litter object positions with an accuracy at decimeter level for objects not too far from locations recognizable in the map.

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FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters

Cited by 58 publications

References 36 publications

Deep LiDAR-Radar-Visual Fusion for Object Detection in Urban Environments

Deep LiDAR-Radar-Visual Fusion for Object Detection in Urban Environments

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

Mini Uav-Based Litter Detection on River Banks

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