Aquatic Robot Design for Water Pollutants Monitoring

This paper focuses on the design and development of an Aquatic Robot for water pollutants monitoring. An aquatic robot is integrated with the smartphone for data acquisition. The implemented design contains CV algorithm for image processing on openCV platform. Regularly monitoring aquatic pollutants is needed for pure aquatic environment and safe aquatic life. The human health and water transport are also main consideration towards this robot design. The proposed Aquatic robot consists of sensors and camera for sensing hazardous pollutants and capturing images of surrounding environment respectively. The aquatic robot can accurately detect pollutants and display results on smartphone in the presence of various conditions. DOI: 10.17762/ijritcc2321-8169.15064

The Geometry and Usage of the Supplementary Fisheye Lenses in Smartphones

Nowadays, mobile phones are more than a device that can only satisfy the communication need between people. Since fisheye lenses integrated with mobile phones are lightweight and easy to use, they are advantageous. In addition to this advantage, it is experimented whether fisheye lens and mobile phone combination can be used in a photogrammetric way, and if so, what will be the result. Fisheye lens equipment used with mobile phones was tested in this study. For this, standard calibration of ‘Olloclip 3 in one’ fisheye lens used with iPhone 4S mobile phone and ‘Nikon FC‐E9’ fisheye lens used with Nikon Coolpix8700 are compared based on equidistant model. This experimental study shows that Olloclip 3 in one fisheye lens developed for mobile phones has at least the similar characteristics with classic fisheye lenses. The dimensions of fisheye lenses used with smart phones are getting smaller and the prices are reducing. Moreover, as verified in this study, the accuracy of fisheye lenses used in smartphones is better than conventional fisheye lenses. The use of smartphones with fisheye lenses will give the possibility of practical applications to ordinary users in the near future

A Vision for Cleaner Rivers: Harnessing Snapshot Hyperspectral Imaging to Detect Macro-Plastic Litter

Plastic waste entering the riverine harms local ecosystems leading to negative ecological and economic impacts. Large parcels of plastic waste are transported from inland to oceans leading to a global scale problem of floating debris fields. In this context, efficient and automatized monitoring of mismanaged plastic waste is paramount. To address this problem, we analyze the feasibility of macro-plastic litter detection using computational imaging approaches in river-like scenarios. We enable near-real-time tracking of partially submerged plastics by using snapshot Visible-Shortwave Infrared hyperspectral imaging. Our experiments indicate that imaging strategies associated with machine learning classification approaches can lead to high detection accuracy even in challenging scenarios, especially when leveraging hyperspectral data and nonlinear classifiers. All code, data, and models are available online: https://github.com/RIVeR-Lab/hyperspectral_macro_plastic_detection

Intelligent Debris Mass Estimation Model for Autonomous Underwater Vehicle

Marine debris poses a significant threat to the survival of marine wildlife, often leading to entanglement and starvation, ultimately resulting in death. Therefore, removing debris from the ocean is crucial to restore the natural balance and allow marine life to thrive. Instance segmentation is an advanced form of object detection that identifies objects and precisely locates and separates them, making it an essential tool for autonomous underwater vehicles (AUVs) to navigate and interact with their underwater environment effectively. AUVs use image segmentation to analyze images captured by their cameras to navigate underwater environments. In this paper, we use instance segmentation to calculate the area of individual objects within an image, we use YOLOV7 in Roboflow to generate a set of bounding boxes for each object in the image with a class label and a confidence score for every detection. A segmentation mask is then created for each object by applying a binary mask to the object's bounding box. The masks are generated by applying a binary threshold to the output of a convolutional neural network trained to segment objects from the background. Finally, refining the segmentation mask for each object is done by applying post-processing techniques such as morphological operations and contour detection, to improve the accuracy and quality of the mask. The process of estimating the area of instance segmentation involves calculating the area of each segmented instance separately and then summing up the areas of all instances to obtain the total area. The calculation is carried out using standard formulas based on the shape of the object, such as rectangles and circles. In cases where the object is complex, the Monte Carlo method is used to estimate the area. This method provides a higher degree of accuracy than traditional methods, especially when using a large number of samples

Remote sensing of water quality in reservoirs and lakes in semi-arid climates

Overlake measurements using aerial cameras (remote sensing) combined with water truth collected from boats most economically provided wide-band photographs rather than precise spectra. With use of false color infrared film (400-950 nm), the reflected spectral signatures seen from hundreds to thousands of meters above the lake merged to produce various color tones. Such colors were easily and inexpensively obtained and could be recognized by lake management personnel without any prior training. The characteristic spectral signatures of various algal types were also recognizable in part by the color tone produced by remote sensing

Water quality monitoring using wireless sensor network and smartphone-based applications: a review

A wireless sensor network (WSN) has a huge potential in water ecology monitoring applications. The integration of WSN to a portable sensing device offers the feasibility of sensing distribution capability, on-site data measurements, and remote sensing abilities. Due to the advancement of WSN technology, unexpected contamination events in water environments can be observed continuously. Local Area Network (LAN), Wireless Local Area Network (WLAN) and Internet web-based are commonly used as a gateway unit for data communication via local base computer using standard Global System for Mobile Communication (GSM) or General Packet Radio Services (GPRS). However, WSN construction is costly and a growing static infrastructure increases the energy consumptions. Hence, a growing trend of smartphone-based application in the field of water monitoring is a surrogate approach to engage mobile base stations for in-field analysis that are driven by the expanding adaptation of Bluetooth, ZigBee and standard Wi-Fi routers. Owing to the fact that smartphones are portable and accessible, mobile data collection from WSN in remote locations are achievable. This paper comprehensively reviews the detection of water contaminants using smartphone-based applications in accordance with WSN technologies. In this paper, some recommendations and prospective views on the developments of water quality monitoring will be discussed