Scale-aware feature pyramid architecture for marine object detection

Marine object detection is an appealing but challengeable task in computer vision. Even though recent popular object detection algorithms perform well on common classes, they cannot acquire satisfied detection performance on marine objects because underwater images are affected by color cast and blur, and scales of the target in underwater images are usually small. These phenomena aggravate the difficulty of detection. Thus, it is urgent to design a proper structure to settle marine object detection issues. To this end, this paper proposes a novel scale-aware feature pyramid architecture named SA-FPN to extract abundant robust features on underwater images and improve the performance on marine object detection. Specifically, we design a special backbone subnetwork to improve the ability of feature extraction, which could provide richer fine-grained features for small object detection. What is more, this paper proposes a multi-scale feature pyramid to enrich the semantic features for prediction. Each feature map is enhanced by the higher level layer with context information through a top-down upsampling pathway. Through obtaining ample feature maps on underwater images, our algorithm could generate multiple bounding boxes for each target. To mitigate the reduplicative boxes and avoid miss suppression, we replace the non-maximum suppression method with soft non-maximum suppression. In this paper, we evaluate our algorithm on underwater image datasets and achieve 76.27% mAP. Meanwhile, we conduct experiments on PASCAL VOC datasets and smart unmanned vending machines datasets and get 79.13% mAP and 91.81% mAP, respectively. The experimental results reveal that our approach achieves best performance not only on marine object detection, but also on common classes.