Hand-Crafted Features for Floating Plastic Detection

Abstract

Plastic waste is a global concern that has a negative impact on the oceans and wildlife health. This paper focuses on detection of floating plastics in aerial images taken from unmanned aerial vehicles (UAVs). It proposes a new method for plastic detection in marine environments, based on SIFT descriptor and color histograms for feature extraction, as an alternative to state-of-the-art object detectors based on convolutional neural networks (CNNs), Our approach is named SURFACE: “SIFT featURes For plAstiC dEtection”. We investigate how different color-spaces and image resolutions impact the extraction of SIFT features and compare SURFACE to ResNet CNN. Also, we provide a detailed comparison with YOLO and Faster-RCNN object detection models and show that SURFACE achieves approximately the same accuracy while being faster and less memory consuming. The dataset acquired during this research will be publicly available.