Automated detection of rockfish in unconstrained underwater videos using Haar cascades and a new image dataset: labeled fishes in the wild

This article presents methods applied for automated detection of fish based on cascade classifiers of Haar-like features created using underwater images from a remotely operated vehicle under ocean survey conditions. The images are unconstrained, and the imaging environment is highly variable due to the moving imaging platform, a complex rocky seabed background, and still and moving cryptic fish targets. These images are released in a new image dataset, labeled fishes in the wild, of in situ groundfishes, mainly rockfishes (Sebastes spp.) and other associated species. The dataset includes an annotated training and validation image set, as well as an independent test video image sequence. Several Haar cascades are developed from the training set and applied to the validation and test video images for evaluation. Based on performance evaluation using the validation set, true positive detection rates of 63 to 89% were achieved for seven classifiers. True positive detection rates for the test video were 66% to 81% for analyst-confirmed fish targets. Detector performance is dependent on training data, training and detection parameters, fish orientation, range to target, variable light intensity and attenuation.