An accurate and robust perception system is key to understanding the driving environment of autonomous robots. Autonomous needs 3-D information about objects, including object’s location pose, understand clearly. A camera sensor widely used in because its richness color texture, low price. The major problem with lack information, which necessary environment. In addition, scale change occlusion make object detection more challenging. Many deep learning-based methods, such as depth estimation,...

The pursuit of autonomous driving relies on developing perception systems capable making accurate, robust, and rapid decisions to interpret the environment effectively. Object detection is crucial for understanding at these systems’ core. While 2D object classification have advanced significantly with advent deep learning (DL) in computer vision (CV) applications, they fall short providing essential depth information, a key element comprehending environments. Consequently, 3D becomes...

Fish species recognition is crucial to identifying the abundance of fish in a specific area, controlling production management, and monitoring ecosystem, especially endangered species, which makes accurate essential. In this work, problem formulated as an object detection model handle multiple single image, challenging classify using simple classification network. The proposed consists MobileNetv3-large VGG16 backbone networks SSD head. Moreover, class-aware loss function solve class...

Three-dimensional object detection is crucial for autonomous driving to understand the environment. Since pooling operation causes information loss in standard CNN, we designed a wavelet-multiresolution-analysis-based 3D network without operation. Additionally, instead of using single filter like convolution, used lower-frequency and higher-frequency coefficients as filter. These filters capture more relevant parts than filter, enlarging receptive field. The model comprises discrete wavelet...

In video-based fish surveys, species recognition plays a vital role in stock assessments, ecosystem analysis, production management, and protection of endangered species. However, implementing detection algorithms underwater environments presents significant challenges due to factors such as varying lighting conditions, water turbidity, the diverse appearances this work, transformer-enhanced YOLOv8 (YOLOv8-TF) is proposed for recognition. The YOLOv8-TF enhances performance by adjusting depth...

Increased necessity to monitor vital fish habitat has resulted in proliferation of camera-based observation methods and advancements camera processing technology. Automated image analysis through computer vision algorithms emerged as a tool for fisheries address big data needs, reduce human intervention, lower costs, improve timeliness. Models have been developed this study with the goal implement such automated commercially important Gulf Mexico species habitats. Further, proposes adapting...

Species recognition is an important aspect of video based surveys, which support stock assessments, inspecting the ecosystem, handling production management, and protecting endangered species. It a challenging task to implement fish species detection algorithms in underwater environments. In this work, we introduce YOLOv5 model for that can be implemented as object analyzing multiple fishes single image. Moreover, have modified depth scale different layers backbone obtain improved results on...

Fish species recognition and detection are essential for fishery industries. Accurate robust classification play a vital role in monitoring fish activities identifying the distribution of specific species, which is to know endangered species. It also controlling production overall ecosystem control management. However, current artificial intelligence technologies, such as deep learning, limited ocean system compared other areas like robotics security. The major challenge building learning...

<p>An accurate and robust perception system is key to understanding the driving environment of autonomous robots. Autonomous needs 3D information about objects, including object’s location pose, understand clearly. A camera sensor widely used in because its richness color, texture, low price. The major problem with lack information, which necessary environment. Additionally, scale change cclusion make object detection more challenging. Many deep learning-based methods, such as depth...

Video surveys are commonly used to monitor the abundance and distribution of managed species support management. However, considerable effort, time, cost required for human review automated fish recognition provides an effective solution remove bottleneck post-processing. Implementing detection techniques underwater imagery is a challenging task. In this work, we present Multiple Instance Active-learning Fish-species Recognition (MI-AFR), which formulated as object detection-based approach...

<p>Autonomous driving requires accurate, robust, and fast decision-making perception systems to understand the environment. Object detection is critical in allowing system The systems, especially 2D object classification, have succeeded because of emergence deep learning (DL) computer vision (CV) applications. However, lacks depth information, which crucial understanding Therefore, 3D fundamental for autonomous robotics applications estimate objects’ location CV community has been...

3D object detection is crucial for autonomous driving to understand the environment. Since pooling operation causes information loss in standard CNN, we have designed a wavelet multiresolution analysis-based network without operation. Additionally, instead of using single filter like convolution, use lower-frequency and higher-frequency coefficients as filter. These filters capture more relevant parts than filter, enlarging receptive field. The model comprises discrete transform (DWT) an...

<p>An accurate and robust perception system is key to understanding the driving environment of autonomous robots. Autonomous needs 3D information about objects, including object’s location pose, understand clearly. A camera sensor widely used in because its richness color, texture, low price. The major problem with lack information, which necessary environment. Additionally, scale change cclusion make object detection more challenging. Many deep learning-based methods, such as depth...

<p>An accurate and robust perception system is key to understanding the driving environment of autonomous robots. Autonomous needs 3D information about objects, including object’s location pose, understand clearly. A camera sensor widely used in because its richness color, texture, low price. The major problem with lack information, which necessary environment. Additionally, scale change cclusion make object detection more challenging. Many deep learning-based methods, such as depth...

Accurate fish species identification is essential for stock assessments, production management, document ecosystem changes, and protection of endangered species. Image processing computer vision techniques have been widely employed detection, classification, tracking, reducing human efforts in these tasks. However, methods often rely on extensive training data with correct annotations. Annotating many images captured from marine environments poses a significant challenge. This work proposes...

Identification of fish species is vital for fisheries management, stock assessments, protection endangered species, and ecosystem management. Image based surveys often deploy video cameras that are used to collect large image datasets reviewed by a human observer identify generate numerical count at each station. One main challenge in labeling or annotating such dataset it requires huge amount time, cost, effort. Recently, general adversarial network (GAN) generative techniques have drawn...

Fish species must be identified for stock assessments, ecosystem monitoring, production management, and the conservation of endangered species. Implementing algorithms fish detection in underwater settings like Gulf Mexico poses a formidable challenge. Active learning, method that efficiently identifies informative samples annotation while staying within budget, has demonstrated its effectiveness context object recent times. In this study, we present an active model designed recognition...

Three-dimensional object detection is vital for understanding the autonomous vehicle driving environment. Different sensors are used this purpose, such as cameras and LiDARs. Camera rich in color texture information. However, unsuitable 3D due to lack of depth Additionally, camera vulnerable bad weather, snow, fog, night driving. Autonomous needs a fast accurate perception system robust operation following pipeline, path planning control. LiDAR commonly sensor because its information reduces...

Baited underwater video sampling is a common method to monitor fish populations, yet the data requirements associated with imagery leads bottlenecks in productivity. Image analysis that incorporates automated methods through deep-learning models could provide solutions. These have potential improve efficiency, and decrease cost of producing information on populations habitats. In order reduce human intervention, these must produce precise, accurate results. While for gauging model...

<p>Autonomous driving requires accurate, robust, and fast decision-making perception systems to understand the environment. Object detection is critical in allowing system The systems, especially 2D object classification, have succeeded because of emergence deep learning (DL) computer vision (CV) applications. However, lacks depth information, which crucial understanding Therefore, 3D fundamental for autonomous robotics applications estimate objects’ location CV community has been...

Global Positioning System (GPS) navigation provides accurate positioning with global coverage, making it a reliable option in open areas unobstructed sky views. However, signal degradation may occur indoor spaces and urban canyons. In contrast, Inertial Measurement Units (IMUs) consist of gyroscopes accelerometers that offer relative motion information such as acceleration rotational changes. Unlike GPS, IMUs do not rely on external signals, them useful GPS-denied environments. Nonetheless,...