engleski

Hand-Crafted Features for Floating Plastic Detection

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.

Sea surface , Memory management , Wildlife , Detectors , Object detection , Feature extraction , Plastics

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