Underwater Object Classification and Detection: first results and open challenges (original) (raw)
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International journal for research in applied science and engineering technology ijraset, 2020
: Detecting underwater objects is challenged by various kinds of aspect ratios, object size,material colour, cluttered backgrounds, and in particular,not defined orientations. In our paper, we are using a Probabilistic Neural Network (PNN) features from layers which are combined to perform orientation robust aerial underwater object detection. We explore the essential characteristics of PNN as well as corelate the extracted features to the principle of disengaging feature learning. An image segmentation based approach is used to localize ROIs of different aspect ratios, and furtherly ROIs are classified into positives or negative using a DCNN features. On inquiring the two datasets collected from Google Earth, we illustrate that the proposed aerial underwater object detection approach is simple and easy process. Fast and robust underwater object detection in aerial images is potentially applicable in traffic surveillance, emergency, remote sensing and large scale image content analysis.
Investigation of Vision-based Underwater Object Detection with Multiple Datasets
International Journal of Advanced Robotic Systems, 2015
In this paper, we investigate the potential of vision-based object detection algorithms in underwater environments using several datasets to highlight the issues arising in different scenarios. Underwater computer vision has to cope with distortion and attenuation due to light propagation in water, and with challenging operating conditions. Scene segmentation and shape recognition in a single image must be carefully designed to achieve robust object detection and to facilitate object pose estimation. We describe a novel multi-feature object detection algorithm conceived to find human-made artefacts lying on the seabed. The proposed method searches for a target object according to a few general criteria that are robust to the underwater context, such as salient colour uniformity and sharp contours. We assess the performance of the proposed algorithm across different underwater datasets. The datasets have been obtained using stereo cameras of different quality, and diverge for the target object type and colour, acquisition depth and conditions. The effectiveness of the proposed approach has been experimentally demonstrated. Finally, object detection is discussed in connection with the simple colour-based segmentation and with the difficulty of tri-dimensional processing on noisy data.
Remote Sensing, 2020
Video image processing and object classification using a Deep Learning Neural Network (DLNN) can significantly increase the autonomy of underwater vehicles. This paper describes the results of a project focused on using DLNN for Object Classification in Underwater Video (OCUV) implemented in a Biomimetic Underwater Vehicle (BUV). The BUV is intended to be used to detect underwater mines, explore shipwrecks or observe the process of corrosion of munitions abandoned on the seabed after World War II. Here, the pretrained DLNNs were used for classification of the following type of objects: fishes, underwater vehicles, divers and obstacles. The results of our research enabled us to estimate the effectiveness of using pretrained DLNNs for classification of different objects under the complex Baltic Sea environment. The Genetic Algorithm (GA) was used to establish tuning parameters of the DLNNs. Three different training methods were compared for AlexNet, then one training method was chosen...
Underwater object detection using Invert Multi-Class Adaboost with deep learning
2020 International Joint Conference on Neural Networks (IJCNN)
In recent years, deep learning based methods have achieved promising performance in standard object detection. However, these methods lack sufficient capabilities to handle underwater object detection due to these challenges: (1) Objects in real applications are usually small and their images are blurry, and (2) images in the underwater datasets and real applications accompany heterogeneous noise. To address these two problems, we first propose a novel neural network architecture, namely Sample-WeIghted hyPEr Network (SWIPENet), for small object detection. SWIPENet consists of high resolution and semanticrich Hyper Feature Maps which can significantly improve small object detection accuracy. In addition, we propose a novel sampleweighted loss function which can model sample weights for SWIPENet, which uses a novel sample re-weighting algorithm, namely Invert Multi-Class Adaboost (IMA), to reduce the influence of noise on the proposed SWIPENet. Experiments on two underwater robot picking contest datasets URPC2017 and URPC2018 show that the proposed SWIPENet+IMA framework achieves better performance in detection accuracy against several state-of-the-art object detection approaches.
CNN-based YOLOv3 Comparison for Underwater Object Detection
International Journal of Electrical & Electronic Systems Research (IEESR), 2021
Object detection that deals with identifying and locating object is one of area that integrate from the advancement in machine learning and computer vision. Modern object detection which carried out supervised learning utilizes Convo-lutional Neural Network (CNN) as the backbone of the detection architecture which is significant for underwater object detection as the underwater images are usually low in quality and blurry. Single stage detection such as You Only Look Once (YOLO) is one the famous object detection model that is prominent among researchers due to high performance in accuracy and processing speed. However, YOLO has many versions where the current incremental improvement model of YOLOv3 has been widely used by researchers to solve different types of problem related to object detection. Therefore, there is a need to explore the trade-off relationship between the processing speed and precision of each YOLO model. In the study, two different open source underwater datasets were used in four different YOLOv3 models namely as YOLOv3-SPP, YOLOv3-Tiny, YOLOv3-Tiny-PRN and the original YOLOv3 in order to study their performance based on metrics evaluation of precision and processing speed (FPS). The result shows that YOLOv3-SPP proved to be the best in terms of precision while YOLOv3-Tiny-PRN lead in terms of execution speed. So, this study shows that YOLOv3 model is highly significant to be implemented and able to accurately detect underwater objects with haze and low-light environment. This study can help researchers and industry in determining the best YOLOv3 model specifically for detection of the underwater images and its application.
An Image-Based Benchmark Dataset and a Novel Object Detector for Water Surface Object Detection
Frontiers in Neurorobotics, 2021
Water surface object detection is one of the most significant tasks in autonomous driving and water surface vision applications. To date, existing public large-scale datasets collected from websites do not focus on specific scenarios. As a characteristic of these datasets, the quantity of the images and instances is also still at a low level. To accelerate the development of water surface autonomous driving, this paper proposes a large-scale, high-quality annotated benchmark dataset, named Water Surface Object Detection Dataset (WSODD), to benchmark different water surface object detection algorithms. The proposed dataset consists of 7,467 water surface images in different water environments, climate conditions, and shooting times. In addition, the dataset comprises a total of 14 common object categories and 21,911 instances. Simultaneously, more specific scenarios are focused on in WSODD. In order to find a straightforward architecture to provide good performance on WSODD, a new ob...
A Benchmark dataset for both underwater image enhancement and underwater object detection
2020
Underwater image enhancement is such an important vision task due to its significance in marine engineering and aquatic robot. It is usually work as a pre-processing step to improve the performance of high level vision tasks such as underwater object detection. Even though many previous works show the underwater image enhancement algorithms can boost the detection accuracy of the detectors, no work specially focus on investigating the relationship between these two tasks. This is mainly because existing underwater datasets lack either bounding box annotations or high quality reference images, based on which detection accuracy or image quality assessment metrics are calculated. To investigate how the underwater image enhancement methods influence the following underwater object detection tasks, in this paper, we provide a large-scale underwater object detection dataset with both bounding box annotations and high quality reference images, namely OUC dataset. The OUC dataset provides a...
Classical and neural network approaches to object detection in underwater robotics competitions
INTERNATIONAL CONFERENCE ON INFORMATICS, TECHNOLOGY, AND ENGINEERING 2021 (InCITE 2021): Leveraging Smart Engineering
The article considers two approaches to detecting underwater objects in the image, i.e. classical approach and neural network approach. Main advantages and disadvantages of each approach are presented. Various approaches to operation quality were analyzed, including assessment of speed and accuracy, as well as identification of preconditions required to achieve the maximum quality. The article also considers preliminary use of image dehazing methods to improve visibility and contrast. Objects for recognition considered in the article are elements of missions in the Singapore Autonomous Underwater Vehicle Challenge (SAUVC) competitions in Singapore. Nvidia Jetson TX2 singleboard computer is the target platform for the proposed methods, analysis of the method speed was carried out both using the graphics processing unit (GPU) for neural network, and without using it in classical and neural network methods in order to obtain potential speed estimate on simpler platforms without the GPU.
Quantitative performance analysis of object detection algorithms on underwater video footage
2012
Object detection in underwater unconstrained environments is useful in domains like marine biology and geology, where the scientists need to study fish populations, underwater geological events etc. However, in literature, very little can be found regarding fish detection in unconstrained underwater videos. Nevertheless, the unconstrained underwater video domain constitutes a perfect soil for bringing state-ofthe-art object detection algorithms to their limits because of the nature of the scenes, which often present with a number of intrinsic di culties (e.g. multi-modal backgrounds, complex textures and color patterns, ever-changing illumination etc..).
A Benchmark for Maritime Object Detection with Centernet on an Improved Dataset, ABOships-PLUS
Journal of Marine Science and Engineering
Object detection from waterborne imagery is an essential aspect in maritime traffic management, navigation safety and coastal security. Building efficient autonomous systems, which can take decisions in critical situations, requires an integration of complex object detectors in real time. Object detectors trained on generic datasets often give unsatisfactory results in complex scenarios like the maritime environment, since only a fraction of their images contain maritime vessels. Publicly available domain-specific datasets are scarce, and they are limited in the number of images and annotations. Compared to object detection in applications such as autonomous vehicles, maritime vessel detection is considerably reduced in computer vision research. This creates a deficit in exhaustive benchmarking studies for maritime detection datasets. To bridge this gap, we relabel the ABOships dataset and benchmark a state-of-the-art center-based detector, Centernet, on the newly relabeled dataset,...