Sattar, JunaedMukherjee, RishiSingh, SakshiMcWilliams, Jack2025-02-272025-02-272025-02-27https://hdl.handle.net/11299/2701461. YOLO File List (all of these files are present in YOLO.zip) A. Filename: images.zip Short description: This compressed folder contains the images of the COU dataset in jpg format split into train, test, and val directories. images - | train - | test - | val - B. Filename: labels.zip Short description: This compressed folder contains the annotation labels of the COU dataset in the YOLO label format split into train, test, and val directories. labels - | train - | test - | val - C. Filename: obj.names Short description: This file contains the class names required for YOLO models. D. Filename: dataset.yaml Short description: This file contains the relevant dataset information for training a YOLO model. 2. Additional Filename: coco.zip Short description: This file contains the dataset in the COCO format. | coco - | train_annotations.json | test_annotations.json | val_annotations.json | images -We introduce COU: Common Objects Underwater, an instance-segmented image dataset of commonly found man-made objects in multiple aquatic and marine environments. COU contains approximately 10K segmented images, annotated from images collected during a number of underwater robot field trials in diverse locations. COU has been created to address the lack of datasets with robust class coverage curated for underwater instance segmentation, which is particularly useful for training light-weight, real-time capable detectors for Autonomous Underwater Vehicles (AUVs). In addition, COU addresses the lack of diversity in object classes since the commonly available aquatic image datasets focus only on marine life. Currently, COU contains images from both closed-water (pool) and open-water (lakes and oceans) environments, of 24 different classes of objects including marine debris, dive tools, and AUVs To assess the efficacy of COU in training underwater object detectors, we use three state-of-the-art models to evaluate its performance and accuracy, using a combination of standard accuracy and efficiency metrics. The improved performance of COU-trained detectors over those solely trained on terrestrial data demonstrates the clear advantage of the availability of annotated underwater images.Attribution-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-sa/4.0/Dataset