Erik Sollesnes

Norwegian University of Science and Technology

Papers

Total Citations

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About

Erik Sollesnes is a researcher at the forefront of autonomous underwater robotics, specializing in computer vision and deep learning for marine exploration. His work addresses the critical challenge of underwater object detection, where poor visibility and light distortion often hinder traditional methods. Sollesnes’s most cited paper, "Underwater Object Detection using Image Enhancement and Deep Learning Models" (2023), introduces a novel pipeline that combines image enhancement techniques with state-of-the-art deep learning architectures to significantly improve detection accuracy in murky waters. This contribution directly supports the deployment of autonomous underwater vehicles (AUVs) in oceanographic mapping, environmental monitoring, and underwater archaeology. With 14 citations in a short time, his work is gaining traction among researchers seeking robust solutions for real-world marine applications. By bridging the gap between image preprocessing and neural network performance, Sollesnes is helping to make AUVs more reliable and autonomous, paving the way for safer, more efficient ocean exploration. His research holds promise for advancing both scientific discovery and industrial operations beneath the waves.