An enhanced YOLOv5 model for fish species recognition from underwater environments

Accurate recognition of multiple fish species is essential in marine ecology and fisheries. Precisely classifying and tracking these species enriches our comprehension of their movement patterns and empowers us to create precise maps of species-specific territories. Such profound insights are pivotal in conserving endangered species, promoting sustainable fishing practices, and preserving marine ecosystems' overall health and equilibrium. To partially address these needs, we present a proposed model that combines YOLOv8 for object detection with ByteTrack for tracking. YOLOv8's oriented bounding boxes help to improve object detection across angles, while ByteTrack's robustness in various scenarios makes it ideal for real-time tracking. Experimental results using the SEAMAPD21 dataset show the model's effectiveness, with YOLOv8n being the lightweight yet modestly accurate option, suitable for constrained environments. The study also identifies challenges in fish tracking, such as lighting variations and fish appearance changes, and proposes solutions for future research. Overall, the proposed model shows promising fish tracking and counting results, which is essential for monitoring marine life.

“…Several published works have utilized the SEAMAPD21 dataset. [24][25][26][27][28][29][30][31] Figure 2. Sample Images from the dataset.…”

Section: Methodsmentioning

confidence: 99%

Multi-fish tracking for marine biodiversity monitoring

Alaba,

Prior,

Shah

et al. 2024

Inconsistency-based active learning with adaptive pseudo-labeling for fish species identification

Nabi,

Shah,

Alaba

et al. 2024

The deep neural network has found widespread application in object detection due to its high accuracy. However, its performance typically depends on the availability of a substantial volume of accurately labeled data. Several active learning approaches have been proposed to reduce the labeling dependency based on the confidence of the detector. Nevertheless, these approaches tend to exhibit biases toward high-performing classes, resulting in datasets that do not adequately represent the testing data. In this study, we introduce a comprehensive framework for active learning that considers both the uncertainty and the robustness of the detector, ensuring superior performance across all classes. The robustness-based score for active learning is calculated using the consistency between an image and its augmented version. Additionally, we leverage pseudo-labeling to mitigate potential distribution drift and enhance model performance. To address the challenge of setting the pseudolabeling threshold, we introduce an adaptive threshold mechanism. This adaptability is crucial, as a fixed threshold can negatively impact performance, particularly for low-performing classes or during the initial stages of training. For our experiment, we employ the Southeast Area Monitoring and Assessment Program Dataset 2021 (SEAMAPD21), comprising 130 fish species classes with 28,328 image samples. The results show that our model outperforms the state-of-the-art method and significantly reduces the annotation cost. Furthermore, we benchmark our model's performance against a public dataset (PASCAL VOC07), showcasing its effectiveness in comparison to existing methods.

Active detection for fish species recognition in underwater environments

Shah,

Nabi,

Alaba

et al. 2024

Fish species must be identified for stock assessments, ecosystem monitoring, production management, and the conservation of endangered species. Implementing algorithms for fish species detection in underwater settings like the Gulf of Mexico poses a formidable challenge. Active learning, a method that efficiently identifies informative samples for annotation while staying within a budget, has demonstrated its effectiveness in the context of object detection in recent times. In this study, we present an active detection model designed for fish species recognition in underwater environments. This model can be employed as an object detection system to effectively lower the expense associated with manual annotation. It uses epistemic uncertainty with evidential deep learning (EDL) and proposes a novel module denoted as model evidence head (MEH) for fish species detection in underwater environments. It employs hierarchical uncertainty aggregation (HUA) to obtain the informativeness of an image. We conducted experiments using a fine-grained and extensive dataset of reef fish collected from the Gulf of Mexico, specifically the Southeast Area Monitoring and Assessment Program Dataset 2021 (SEAMAPD21). The experimental results demonstrate that an active detection framework achieves better detection performance on the SEAMAPD21 dataset demonstrating a favorable balance between performance and data efficiency for underwater fish species recognition.