Automatic analysis of deep-water remotely operated vehicle footage for estimation of Norway lobster abundance
; Yee, L.
; Campos, A.C.
Frontiers of Information Technology & Electronic Engineering Vol. 19, Nº 8, pp. 1042 - 1055, August, 2018.
ISSN (print): 2095-9184
ISSN (online): 2095-9230
Journal Impact Factor: 0,910 (in )
Digital Object Identifier: 10.1631/FITEE.1700720
Underwater imaging is being used increasingly by marine biologists as a means to assess the abundance of marine resources and their biodiversity. Previously, we developed the first automatic approach for estimating the abundance of Norway lobsters and counting their burrows in video sequences captured using a monochrome camera mounted on trawling gear. In this paper, an alternative framework is proposed and tested using deep-water video sequences acquired via a remotely operated vehicle. The proposed framework consists of four modules: (1) preprocessing, (2) object detection and classification, (3) object-tracking, and (4) quantification. Encouraging results were obtained from available test videos for the automatic video-based abundance estimation in comparison with manual counts by human experts (ground truth). For the available test set, the proposed system achieved 100% precision and recall for lobster counting, and around 83% precision and recall for burrow detection.