基于深度学习的金枪鱼延绳钓渔获图像识别技术分析

Analysis of deep learning-based tuna longline catch image recognition technique

摘要:

为了实现金枪鱼延绳钓渔获物的高效识别和分类,改善渔业资源监测的准确性,本研究探索了基于卷积神经网络的鱼类图像识别方法。采用上海海洋大学“淞航”号在中西太平洋公海调查中捕获的3种经济性鱼类及10种兼捕渔获物的图像数据,运用单发多箱探测器（Single shot multibox detector,SSD）卷积神经网络进行图像分类识别。通过将局部鱼类图像与整体图片数据集进行比较分析,优化训练数据集,以提升模型的分类性能。结果表明：改进后的鱼类图像数据集在SSD模型上的分类准确率达91.6%,相较于原始数据集提高了6.2%。利用优化后的数据集,SSD模型能够显著提高金枪鱼延绳钓渔获物的识别准确性,具备更好的稳定性和适应性。本研究为基于卷积神经网络的渔业资源监测提供了有效的技术路径,尤其在提升金枪鱼延绳钓渔获物自动分类识别精度方面展现了广泛的应用潜力,对于促进可持续渔业管理和海洋生态保护具有重要意义。

关键词:金枪鱼延绳钓;渔获物识别;卷积神经网络;数据集优化;中西太平洋

Abstract:

In order to achieve efficient identification and classification of tuna longline catches and to improve the accuracy of fishery resources monitoring, this study explores a fish image recognition method based on convolutional neural network. The experiments were conducted using image data of three economic fish species and ten bycatch species caught by the Songhang of Shanghai Ocean University during the high seas survey in the western and central Pacific Ocean, and a convolutional neural network （CNN） based on a single shot multiBox detector （SSD） was used to classify and recognise the images. The training dataset is optimised by comparing and analysing the local fish images with the overall image dataset to improve the classification performance of the model. The experimental results show that the classification accuracy of the improved fish image dataset on the SSD model reaches 91.6%, which is a 6.2% improvement compared to the original dataset. The study shows that using the optimised dataset, the SSD model can significantly improve the recognition accuracy of tuna longline catches with better stability and adaptability. This study provides an effective technical path for fishery resource monitoring based on convolutional neural networks, especially in improving the automatic classification and identification accuracy of tuna longline catches, which is of great significance for promoting sustainable fishery management and marine ecological protection.

Key words:tuna longlining;catch identification;convolutional neural networks;dataset optimisation;Western and Central Pacific Ocean