A Comparison of Deep Learning Approach for Underwater Object
Detection

A Comparison of Deep Learning Approach for Underwater Object
Detection

: Underwater Object Detection, Faster-RCNN, SSD, RetinaNet, YOLOv3, YOLOv4

n recent year, marine ecosystems and fisheries becomes potential resources, therefore, monitoring of these objects
will be important to ensure their existence. One of computer vision techniques, it is object detection, utilized to
recognize and localize objects in underwater scenery. Many studies have been conducted to investigate various
deep learning methods implemented in underwater object detection; however, only a few investigations have been
performed to compare mainstream object detection algorithms in these circumstances. This article examines
various state-of-the-art deep learning methods applied to underwater object detection, including Faster-RCNN,
SSD, RetinaNet, YOLOv3, and YOLOv4. We trained five models on RUIE dataset, then the average detection time
used to compare how fast a model can detect object within an image; and mAP also applied to measured detection
accuracy. All trained models have costs and benefits; SSD was fast but had poor performance; RetinaNet had
consistent performance across different thresholds but the detection speed was slow; YOLOv3 was the fastest and
had sufficient performance comparable with RetinaNet; YOLOv4 was good at first but performance dropped as
threshold enlargement; also, YOLOv4 needed extra time to detect objects compared to YOLOv3. There are no
models that are fully suited for underwater object detection; nonetheless, when the mAP and average detection
time of the five models were compared, we determined that YOLOv3 is the best acceptable model among the
evaluated underwater object detection models.

Nurcahyani Wulandari1
, Igi Ardiyanto2
, Hanung Adi Nugroho3

Universitas Gadjah Mada

20-04-2022

Fajar Bagus W

PDF

Indonesia

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