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Publications
FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters
Marine debris is severely threatening the marine lives and causing sustained pollution to the whole ecosystem. To prevent the wastes from ge… (see more)tting into the ocean, it is helpful to clean up the floating wastes in inland waters using the autonomous cleaning devices like unmanned surface vehicles. The cleaning efficiency relies on a high-accurate and robust object detection system. However, the small size of the target, the strong light reflection over water surface, and the reflection of other objects on bank-side all bring challenges to the vision-based object detection system. To promote the practical application for autonomous floating wastes cleaning, we present FloW†, the first dataset for floating waste detection in inland water areas. The dataset consists of an image sub-dataset FloW-Img and a multimodal sub-dataset FloW-RI which contains synchronized millimeter wave radar data and images. Accurate annotations for images and radar data are provided, supporting floating waste detection strategies based on image, radar data, and the fusion of two sensors. We perform several baseline experiments on our dataset, including vision-based and radar-based detection methods. The results show that, the detection accuracy is relatively low and floating waste detection still remains a challenging task.
2021-10-01
IEEE International Conference on Computer Vision (published)
Spinal cord cross-sectional area (CSA) is a relevant biomarker to assess spinal cord atrophy in various neurodegenerative diseases. However,… (see more) the considerable inter-subject variability among healthy participants currently limits its usage. Previous studies explored factors contributing to the variability, yet the normalization models were based on a relatively limited number of participants (typically 300 participants), required manual intervention, and were not implemented in an open-access comprehensive analysis pipeline. Another limitation is related to the imprecise prediction of the spinal levels when using vertebral levels as a reference; a question never addressed before in the search for a normalization method. In this study we implemented a method to measure CSA automatically from a spatial reference based on the central nervous system (the pontomedullary junction, PMJ), we investigated various factors to explain variability, and we developed normalization strategies on a large cohort (N=804). Cervical spinal cord CSA was computed on T1w MRI scans for 804 participants from the UK Biobank database. In addition to computing cross-sectional at the C2-C3 vertebral disc, it was also measured at 64 mm caudal from the PMJ. The effect of various biological, demographic and anatomical factors was explored by computing Pearson’s correlation coefficients. A stepwise linear regression found significant predictors; the coefficients of the best fit model were used to normalize CSA. The correlation between CSA measured at C2-C3 and using the PMJ was y = 0.98x + 1.78 (R2 = 0.97). The best normalization model included thalamus volume, brain volume, sex and interaction between brain volume and sex. With this model, the coefficient of variation went down from 10.09% (without normalization) to 8.59%, a reduction of 14.85%. In this study we identified factors explaining inter-subject variability of spinal cord CSA over a large cohort of participants, and developed a normalization model to reduce the variability. We implemented an approach, based on the PMJ, to measure CSA to overcome limitations associated with the vertebral reference. This approach warrants further validation, especially in longitudinal cohorts. The PMJ-based method and normalization models are readily available in the Spinal Cord Toolbox.
