基于GA-RetinaNet的水下目标检测

水下目标自动检测方法对海洋智能捕捞工作发挥着重要作用,针对现有目标检测方法存在的对水下生物检测精度不高问题,提出了一种GA-RetinaNet算法的水下目标检测方法.首先,针对水下图像存在密集目标的特点,通过引入分组卷积替换普通卷积,在不增加参数复杂度的基础上得到更多特征图,提高模型的检测精度;其次,根据水下生物多为小目标生物的特点,引入上下文特征金字塔模块(AC-FPN),利用上下文提取模块保证高分辨率输入的同时获得多个感受野,提取到更多上下文信息,并通过上下文注意力模块和内容注意力模块从中捕获有用特征,准确定位到目标位置.实验结果显示,选用URPC2021数据集进行实验,改进的GA-RetinaNet算法比原算法检测精度提高了2.3%.相比其他主流模型,该算法对不同类型的水下目标均获得了较好的检测结果,检测精度有较大提升.

Underwater Object Detection Based on GA-RetinaNet

Automatic underwater object detection methods play an important role in intelligent marine fishing. To address the problem that the existing object detection methods are not accurate enough for underwater biological detection, this study proposes an underwater object detection method based on the GA-RetinaNet algorithm. Firstly, according to the existence of dense objects in underwater images, the study introduces group convolution to replace ordinary convolution, which can provide more feature information without increasing the complexity of parameters and thereby improve the accuracy of the model. Secondly, according to the characteristic that underwater objects are mostly small objects, the attention-guided context feature pyramid network (AC-FPN) is introduced. The context extraction module is used to obtain more receptive fields while guaranteeing high-resolution inputs and thus extract more contextual information. The context attention module and the content attention module are utilized to capture useful features for the accurate positioning of the object. Experimental results show that the improved GA-RetinaNet algorithm enhances the detection accuracy by 2.3% compared with the original algorithm when the URPC2021 dataset is selected. Compared with other mainstream models, the GA-RetinaNet algorithm achieves better detection results for different types of underwater objects, and the detection accuracy is greatly improved.