ghitri, hamzaElhadj Mimoune, Mouradbelgrana, Fatima Zohra2025-07-232025-07-232025http://dspace.univ-temouchent.edu.dz/handle/123456789/6730In the context of underwater inspection and environmental monitoring, this thesis presents our start-up project “NeptuNet,” a deep learning-based system designed for the automatic identification of underwater gas leaks through marker object detection. The proposed system offers two complementary solutions: a lightweight model, YOLOv5n, suitable for real-time deployment on embedded hardware, and a high-precision model, Faster R-CNN with ResNet-50 backbone, intended for offline analysis. Our approach relies on using computer vision techniques to address the challenges posed by underwater images, such as low contrast and turbidity. The experiments focused on the detection of gas pipes (for navigation assistance), bubble plumes for identifying active leaks, and bubbles considered as a potential leak indicator. Finally, the system was deployed as a web platform, allowing users to test the detection models on their own images or videos.frVision par ordinateur, Traitement et analyse d’images sous-marines, Apprentissage profond(AP), YOLOv5n, Faster R-CNN, Détection de fuites de gaz, Détection d’objets sous-marins, Panaches de bulles, Tuyaux de gaz, Systèmes embarqués.Computer Vision, Underwater Image Processing and Analysis, Deep Learning (DL), YOLOv5n, Faster R-CNN, Gas Leak Detection, Underwater Object Detection, Bubble Plumes, Gas Pipes, Embedded Systems.Thesis