An Intelligent Fishery Detection Method Based on Cross-Domain Image Feature Fusion

• Published in: Fishes 2024-09, Vol.9 (9), p.338
• Main Authors: Xie, Yunjie, Xiang, Jian, Li, Xiaoyong, Chen, Yang
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Aquaculture; Artificial intelligence; Commercial fishing; Comparative analysis; decoupled detection head; Deep learning; Ecological monitoring; Environmental monitoring; Fish; Fish detection; Fisheries; Fishery industry; Fishery resources; Fishes; Fishing; Identification and classification; Machine learning; multi-scale feature fusion; Neural networks; reinforcement feature
• ISSN: 2410-3888; 2410-3888
• DOI: 10.3390/fishes9090338

Target detection technology plays a crucial role in fishery ecological monitoring, fishery diversity research, and intelligent aquaculture. Deep learning, with its distinct advantages, provides significant convenience to the fishery industry. However, it still faces various challenges in practical applications, such as significant differences in image species and image blurring. To address these issues, this study proposes a multi-scale, multi-level, and multi-stage cross-domain feature fusion model. In order to train the model more effectively, a new data set called Fish52 (multi-scene fish data set, a data set containing 52 fish species) was constructed, on which the model achieved an mAP (mean average precision is a key measure of model performance) of 82.57%. Furthermore, we compared prevalent one-stage and two-stage detection methods on the Lahatan (single-scene fish data set) and Fish30 data set (a data set containing 30 fish species) and tested them on the F4k (Fish4Knowledge (F4K) is a data set focused on fish detection and identification) and FishNet data set (it is a data set containing 94,532 images from 17,357 aquatic species). The mAP of our proposed model on the Fish30, Lahatan, F4k, and FishNet data sets reaches 91.72%, 98.7%, 88.6%, and 81.5%, respectively, outperforming existing mainstream models. Comprehensive empirical analysis indicates that our model possesses a high generalization ability and reaches advanced performance levels. In this study, the depth of the model backbone is deepened, a novel neck structure is proposed, and a new module is embedded therein. To enhance the fusion ability of the model, a new attention mechanism module is introduced. In addition, in the adaptive decoupling detection head module, introducing classes with independent parameters and regression adapters reduces interaction between different tasks. The proposed model can better monitor fishery resources and enhance aquaculture efficiency. It not only provides an effective approach for fish detection but also has certain reference significance for the identification of similar targets in other environments and offers assistance for the construction of smart fisheries and digital fisheries.