3D object detection
The \textit{Plectropomus leopardus (P. leopardus)}, a species found in underwater environments, possesses substantial strategic importance due to its rich underwater resources. However, the natural habitat and industrial breeding environment of \textit{P. leopardus} is generally dark and complex, which presents notable challenges to object detection and recognition. In this research, we propose Plectropomus Leopardus recognition using Global Attention mechanism and Transfer learning(PLGAT), integrating a Global Attention Mechanism (GAM) with Transfer Learning to recognize \textit{P.
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One of the key problems in 3D object detection is to reduce the accuracy gap between methods based on LiDAR sensors and those based on monocular cameras. A recently proposed framework for monocular 3D detection based on Pseudo-Stereo has received considerable attention in the community. However, three problems have been discovered in existing practices: (1) relying on a high-performance monocular depth estimator, (2) the generated image suffering from visual holes, deformations, and artifacts, and (3) being difficult to be compatible with geometry-based stereo detectors.
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Safety of the Intended Functionality (SOTIF) addresses sensor performance limitations and deep learning-based object detection insufficiencies to ensure the intended functionality of Automated Driving Systems (ADS). This paper presents a methodology examining the adaptability and performance evaluation of the 3D object detection methods on a LiDAR point cloud dataset generated by simulating a SOTIF-related Use Case.
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