ABSTRACT Marine Debris is the submerged and floating litter that majorly consists of plastics or other manmade polymers that are either intentionally discarded or unknowingly lost along shores of beaches, rivers or other waterbodies. Significant water pollution is caused due to such untreated waste released into the oceans, specifically in the coastal regions. The recent years have seen an immense rise in marine debris, which has created a necessity to reduce the amount of human effort and led to an increase in the demand for automatic marine debris detection. This paper proposes a multi-feature pyramid network (MFPN) of three subnetworks feature extractor, feature pyramid and pooling block that are concatenated to form a single end-to-end network for the task of automatic marine debris detection from multispectral imagery. Further, the feature extractor network is initialized with the pre-trained weights of Resnet-101 to achieve high performance with lower computational resources and normalization is used to reduce the bias of the model. The proposed model is tested on the Sentinel-2-based marine debris benchmark dataset MARIDA and outperforms the state-of-the-art architectures in the field by reporting an mIOU, Pixel Accuracy and F1-Score of 0.71, 0.84 and 0.80, respectively.