Convolutional Neural Networks for Detecting Ship Hatch Closing Moment: A Case Study Using YOLO Family
Resumo
This paper presents a neural network-based computer vision approach for detecting ship cargo-hold hatch closure. The investigation is relevant since weather conditions, especially rainfall, cause damage to cargo such as sodium sulfate, sugar, corn, corn bran, and potassium chloride, among others. Registering when the cargo hold hatch is closed could prevent damage to the cargo, avoiding prejudice to transportation companies. Our proposal uses YOLO framework vision detection as an economical alternative to the current state-of-the-art for detecting ship hatch closing with expensive and complex solutions. This investigation presents an experiment in a tailored dataset, and results are applied to real-time video detection that validates a stable and accurate solution to the problem of ship hatch detection. Results have shown that even though regular YOLO v4 reaches better metrics, with an accuracy of 91.55%, Fast YOLO v4 is better for real-time detection but with a penalty of lower accuracy.
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