Augmented weighted bidirectional feature pyramid network for marine object detection

Abstract

Detecting marine object is an attractive but challenging task in ocean exploration and conservation. Although the current popular object detection algorithms perform well in general. Because underwater images are affected by color projections and the scale of the object is usually smaller, the detection performance for marine object is not ideal. Therefore, this paper proposes an augmented weighted bidirectional feature pyramid network (AWBiFPN) that reduces the weakening of underwater image features and improves the integration efficiency of multi-scale feature, improving marine object detection performance. Specifically, this paper designs a multi-scale feature pyramid for efficient integration of feature information at all levels by combining weighted bidirectional integration pathway with designed Consistent Supervision module. Using Residual Feature Augmentation module to enhance the extraction of unchanging proportional contextual information, reduce the loss of information at the highest level of the feature map in the pyramid network and provide richer particulate characteristics for small-target detection. To scale the feature map and effectively utilize the feature information of different channels in the same spatial location, this article proposes the AWBiFPN layer, which replaces the depthwise separable convolution in the original BiFPN layer with conventional convolution. Evaluated on underwater image dataset (UTDAC), only 12 epochs are trained to 81.63% of mAP; experimented on MS COCO benchmark, achieving 37.9 AP. Meanwhile, the accuracy of the Aquarium Life dataset (AMLD) and underwater detection dataset under natural light (RUOD) are 81.41% mAP and 83.62% mAP, respectively. The results show that the method performed better not only in marine object detection but also in general class detection.