Video surveys are commonly used to monitor the abundance and distribution of managed species to support management. However, considerable effort, time, and cost are required for human review and automated fish species recognition provides an effective solution to remove the bottleneck of post-processing. Implementing fish species detection techniques for underwater imagery is a challenging task. In this work, we present the Multiple Instance Active-learning for Fish-species Recognition (MI-AFR), which is formulated as an object detection-based approach to perform localization and classification of fish species. It can select the most informative fish images from unlabeled sets by estimating the uncertainty of unlabeled images by using adversarial classifiers trained on labeled sets. Moreover, we have analyzed the improved performance of MI-AFR by considering different backbone networks as a trade-off between speed and accuracy. For experiments, we have used the fine-grained and large-scale reef fish dataset obtained from the Gulf of Mexico – the Southeast Area Monitoring and Assessment Program Dataset 2021 (SEAMAPD21). The experimental results illustrate that the superiority of the proposed method can establish a solid foundation for active learning in fish species recognition, especially with a small number of labeled sets.
Fish species recognition and detection are essential for fishery industries. Accurate and robust species classification and detection play a vital role in monitoring fish activities and identifying the distribution of a specific species, which is vital to know the endangered species. It is also essential for controlling production and overall ecosystem control and management. However, the role of current artificial intelligence technologies, such as deep learning, is limited in the ocean system compared to other areas like robotics and security. The major challenge in building a deep learning network is data availability, time, and cost of annotation and labeling. In this work, we build a semi-supervised deep-learning network to recognize fish species. The model is based on a student-teacher network where the teacher network generates pseudo-labels, and the student network is trained with the generated pseud-labels and the labeled data simultaneously. The student network updates the teacher network via an exponential moving average method. The model consists of a faster R-CNN with a feature pyramid network detector. The experimental result of the model on the challenging fish dataset shows a promising result for building semi-supervised object detection models.
Species recognition is an important aspect of video based surveys, which support stock assessments, inspecting the ecosystem, handling production management, and protecting endangered species. It is a challenging task to implement fish species detection algorithms in underwater environments. In this work, we introduce the YOLOv5 model for the recognition of fish species that can be implemented as an object detection model for analyzing multiple fishes in a single image. Moreover, we have modified the depth scale of different layers in the backbone of the YOLOv5 model to obtain improved results on fish species recognition. In addition, we have implemented a transformer block in the backbone network and introduced a class balance loss function to obtain enhanced performance. It can perform fish species recognition as an object detection approach by classifying each of the fish species in addition to localizing for the estimation of the position and size of the fish in an image. Experiments are conducted on the fine-grained and large-scale reef fish dataset that we have obtained from the Gulf of Mexico – the Southeast Area Monitoring and Assessment Program Dataset 2021 (SEAMAPD21). The experimental results demonstrate that an enhanced YOLOv5 model can yield better detection results in comparison to YOLOv5 for underwater fish species recognition.
Collaborative representation classifier (CRC) is an efficient classifier for hyperspectral imagery. It represents a testing sample using labeled ones, and the testing sample is assigned to the class whose labeled samples yield the minimum representation error. The CRC allows all the samples to have equal chance to participate in the representation by imposing an L2 norm minimization constraint. The solution has a closed form, offering computational convenience. Various techniques have been developed for further improvement of CRC-based classifiers, and probabilistic collaborative representation-based classifier (ProCRC) is one of techniques to enhance CRC by using maximum likelihood concept of testing sample that belongs to multiple classes. Taking into consideration for distance-weighted Tikhonov regularization, probabilistic collaborative representation-based classifier with Tikhonov regularization (ProCRT) can enhance the performance of the original ProCRC. In this paper, spatial regularization term is added in the objective function to incorporate spatial information, and the resulting spatial-aware ProCRC (SaProCRC) and spatial-aware ProCRT (SaProCRT) can offer even better classification accuracy with comparable computational cost.
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