Application of Feature Pyramid Network and Feature Fusion Single Shot Multibox Detector for Real-Time Prostate Capsule Detection

Abstract

In the process of feature propagation, the low-level convolution layers of the forward feature propagation network lack semantic information, and information loss occurs when fine-grained information is transferred to higher-level convolution; therefore, multi-stage feature fusion networks are needed to solve the interaction between low-level convolution layers and high-level convolution layers. Based on a two-way feature feedback network and feature fusion mechanism, we created a new object detection network called Feature Pyramid Network (FPN)-based Feature Fusion Single Shot Multibox Detector (FFSSD). A bottom-up and top-down architecture with lateral connections enhances the detector’s ability to extract features, then high-level multi-scale semantic feature maps are utilized to generate a feature pyramid network. The results show that the proposed network the mAP for prostate capsule image detection reaches 83.58%, providing real-time detection ability. The context interaction mechanism can transfer high-level semantic information to low-level convolution, and the resulting convolution after low-level and high-level fusion contains richer location and semantic information.