Aquatic Debris Detection Using Embedded Camera Sensors

Wang, Yong; Wang, Dianhong; Luo, Dang; Wu, Fang

doi:10.3390/s150203116

Cited by 25 publications

(12 citation statements)

References 29 publications

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“…Some methods also use sparsity priors and implement background detection problem as sparse image reconstruction problem [145], [166], [167], [168], [169]. Alternatively, compressive sampling based approaches can be used to reduce dimensionality of the data before using other background detection techniques for reducing the computational cost [25], [170].…”

Section: Approachmentioning

confidence: 99%

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

Prasad

Rajan

Rachmawati³

et al. 2017

IEEE Trans. Intell. Transport. Syst.

307

168

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We present a survey on maritime object detection and tracking approaches, which are essential for the development of a navigational system for autonomous ships. The electro-optical (EO) sensor considered here is a video camera that operates in the visible or the infrared spectra, which conventionally complement radar and sonar and have demonstrated effectiveness for situational awareness at sea has demonstrated its effectiveness over the last few years. This paper provides a comprehensive overview of various approaches of video processing for object detection and tracking in the maritime environment. We follow an approachbased taxonomy wherein the advantages and limitations of each approach are compared. The object detection system consists of the following modules: horizon detection, static background subtraction and foreground segmentation. Each of these has been studied extensively in maritime situations and has been shown to be challenging due to the presence of background motion especially due to waves and wakes. The main processes involved in object tracking include video frame registration, dynamic background subtraction, and the object tracking algorithm itself. The challenges for robust tracking arise due to camera motion, dynamic background and low contrast of tracked object, possibly due to environmental degradation. The survey also discusses multisensor approaches and commercial maritime systems that use EO sensors. The survey also highlights methods from computer vision research which hold promise to perform well in maritime EO data processing. Performance of several maritime and computer vision techniques is evaluated on newly proposed Singapore Marine Dataset.• the difficulty in modeling the dynamics of water (including waves, wakes and foams) for background subtraction and detection of foreground objects, • variations in object appearances due to distance and angle of viewing, and • changes in illumination and weather conditions, such as due to clouds, sunshine, rain, glint, etc.

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Section: Approachmentioning

confidence: 99%

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

Prasad

Rajan

Rachmawati³

et al. 2017

IEEE Trans. Intell. Transport. Syst.

307

168

View full text Add to dashboard Cite

show abstract

“…The dynamic modelling is implemented towards a multi-joint swimming robotic fish developed in our laboratory. The robotic fish [2] is designed as a streamlined shape inspired by an Esoxlucius, whose mechanical structure and appearance are illustrated in Fig. 1.…”

Section: Ivliterature Surveymentioning

confidence: 99%

Aquatic Debris Monitoring & Detection using Raspberry Pi based ‘AQUABOT’

Ghorpade¹,

R.²

2017

International Journal of Innovative Research in Electrical, Ele

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Monitoring aquatic debris is of great interest to the ecosystems, marine life, human health, and water transport. This paper presents the design and implementation of AQUABOTa vision-based surveillance robot system that integrates raspberry pi, robotic fish model along with camera and other sensors for debris monitoring in relatively calm waters. AQUABOT features real-time debris detection and coverage-based rotation scheduling algorithms. The image processing algorithms for debris detection are specifically designed to address the unique challenges in aquatic environments. The rotation scheduling algorithm provides effective coverage for sporadic debris arrivals despite camera"s limited angular view. In this paper, we focus on the design of debris detection and mobility scheduling algorithms running on a single RASP node. The sensing results of multiple nodes can be sent back to a central server via the long-range communication interface for fusion and human inspection.

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“…The characteristic enables a much less power dissipation of 10 W in stand-by mode. In [19], the authors introduce an UW sensor node equipped with an embedded camera. Utilizing this platform, the authors present a speedy and much correct debris detection algorithm, based on compressive sensing theory to consider the challenges of UWA environments.…”

Section: Related Workmentioning

confidence: 99%

SPARCO: Stochastic Performance Analysis with Reliability and Cooperation for Underwater Wireless Sensor Networks

Ahmed¹,

Javaid²,

Ahmad³

et al. 2016

Journal of Sensors

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Reliability is a key factor for application-oriented Underwater Sensor Networks (UWSNs) which are utilized for gaining certain objectives and a demand always exists for efficient data routing mechanisms. Cooperative routing is a promising technique which utilizes the broadcast feature of wireless medium and forwards data with cooperation using sensor nodes as relays. Here, we present a cooperation-based routing protocol for underwater networks to enhance their performance called Stochastic Performance Analysis with Reliability and Cooperation (SPARCO). Cooperative communication is explored in order to design an energy-efficient routing scheme for UWSNs. Each node of the network is assumed to be consisting of a single omnidirectional antenna and multiple nodes cooperatively forward their transmissions taking advantage of spatial diversity to reduce energy consumption. Both multihop and single-hop schemes are exploited which contribute to lowering of path-losses present in the channels connecting nodes and forwarding of data. Simulations demonstrate that SPARCO protocol functions better regarding end-to-end delay, network lifetime, and energy consumption comparative to noncooperative routing protocol—improved Adaptive Mobility of Courier nodes in Threshold-optimized Depth-based routing (iAMCTD). The performance is also compared with three cooperation-based routing protocols for UWSN: Cognitive Cooperation (Cog-Coop), Cooperative Depth-Based Routing (CoDBR), and Cooperative Partner Node Selection Criteria for Cooperative Routing (Coop Re and dth).

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Aquatic Debris Detection Using Embedded Camera Sensors

Cited by 25 publications

References 29 publications

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

Aquatic Debris Monitoring & Detection using Raspberry Pi based ‘AQUABOT’

SPARCO: Stochastic Performance Analysis with Reliability and Cooperation for Underwater Wireless Sensor Networks

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