轻量级水下目标检测器LUDet

LUDet:A lightweight underwater object detector

针对传统水下目标检测器受环境影响较大的问题,使用一种新的轻量级网络LUNet提取特征,结合两阶段检测算法提出轻量级检测器LUDet。首先,网络的第1个阶段使用高效卷积池化来获取不同特征表达。然后,在稠密连接结构的基础上增加两路稠密连接,以提高网络表征能力。网络由卷积池化层与两路稠密连接结构构成,网络中使用GhostModel代替1×1点卷积。使用CAFIR10和CAFIR100数据集进行分类实验验证了提出的骨干网的有效性。针对检测任务,LUDet通过通道注意力、多阶段融合后的特征图对目标进行检测。使用2个水下数据集对改进的检测器进行验证,水下生物数据集上检测的mAP达到了52.5%,水下垃圾数据集上检测的mAP达到了58.7%。

Aiming at the problem that the traditional underwater object detector is greatly affected by the environment,a new lightweight network,called LUNet,is proposed to extract features,and a lightweight detector,called LUDet,is proposed by combining the two-stage detection algorithm.Firstly,in the first stage of the backbone network,efficient convolution pooling is used to obtain different feature expressions.Secondly,two-way dense connections are proposed on the basis of dense connection structure to improve the network representation ability.The network is composed of convolution pool layer and two dense connection structures.GhostModel is used to replace the 1×1 point convolution in the network.The classification experiments on CAFIR10 and CAFIR100 datasets show the effectiveness of the proposed backbone network.For the detection task,LUDet detects the target through feature maps obtained after channel attention and multi-stage fusion.The improved detection algorithm is validated using two underwater datasets.The mAP of the u nderwater biological dataset reaches 52.5%,and the mAP of the underwater garbage dataset reaches 58.7%.