In marine science, there is a need for tools for population counting of species.
Through this thesis we aim to achieve the follow three objectives: first, briefly
discuss the state-of-the-art object detectors that can be used for the detection
of porpoises in drone images/videos. Second, test and compare a few stateof-the-art object detectors in both quantitative and qualitative manner. Third,
based on our results propose a set of suggestions that can be used for future
studies associated with population counting. To answer the second question
we compared three state-of-the-art object detection models, two single-stage
detectors, and one two-shot detector. The models chosen were the Faster RCNN, YOLOv4 and EfficientDet models, and they were trained and tested on a
custom data-set consisting of 7300 labeled images of porpoises, where as 2300
of these were included in the test data set.
Through our experiments, we have discovered that YOLOv4 outperforms Faster
R-CNN and EfficientDet D1 with detection, where YOLO achieves a recall of
97%, compared to 80% recall with EfficientDet D1 and 75% recall with Faster
R-CNN. We also find the average precision @50 values of YOLOv4 to be
0.778, which is greater than EfficientDet D1 with 0.695 and Faster R-CNN with
0.686. Through both qualitative and quantitative methods we discover that
both EfficientDet D1 and Faster R-CNN suffers from poor recall especially when
porpoises overlap in the images. In the case of Faster R-CNN it misses nearly all
detections when the porpoises overlap, but rarely non-overlapping detections.
EfficientDet misses a significant portion of the overlapping detections, but
also misses a few of the singular. Through examination of the COCO detection
metrics, which favor bounding box accuracy, we also show that Faster R-CNN has
more precise bounding boxes than YOLOv4 and EfficientDet D1 by comparing
the less strict AP@50 values, with the stricter AP@75 and [.50 : .05 : .95]
values.
These results imply that a one-stage detection model in YOLOv4 could be used
for object detection of porpoises from drone images. Based on the results, a
few important areas for further investigation is outlined in the discussion, and
a framework was developed which allows marine researches to easily perform
porpoise detection from images and videos
