6 Citations (Scopus)

Abstract

Touchless fish freshness monitoring is an appropriate approach to avoid the destruction of the fish due to fingering or bacterial contamination from the hands of consumers. A freshness monitoring system that classifies the fish's freshness based on body parts requires an object detection model, such as You Only Look Once (YOLO), for detecting the head and tail of fish. Yolov4-Tiny is the recent tiny version of YOLO that has the advantage of a straightforward and fast in detecting objects. However, Yolov4-Tiny obtain lower performance in object detection since the lack of diverse features generated by the backbone from the consecutive convolution layer. This paper proposes modifying Yolov4-Tiny by inserting Spatial Pyramid Pooling (SPP) to expand the variety of feature maps using various kernel pooling from the same convolution layer. Our experimental results show that SPP increases the Recall of the model in detecting the expected object up to 83.42% and Recall up to 68.51% compared to the original versions.

Original languageEnglish
Title of host publicationICAICST 2021 - 2021 International Conference on Artificial Intelligence and Computer Science Technology
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages157-161
Number of pages5
ISBN (Electronic)9781665424042
DOIs
Publication statusPublished - 29 Jun 2021
Event2021 International Conference on Artificial Intelligence and Computer Science Technology, ICAICST 2021 - Virtual, Online
Duration: 29 Jun 2021 → …

Publication series

NameICAICST 2021 - 2021 International Conference on Artificial Intelligence and Computer Science Technology

Conference

Conference2021 International Conference on Artificial Intelligence and Computer Science Technology, ICAICST 2021
CityVirtual, Online
Period29/06/21 → …

Keywords

  • head
  • object detection
  • spatial pyramid pooling
  • tail
  • yolov4-Tiny

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