TY - GEN
T1 - An Algorithm for Selecting the Head and Tail of an Intact Fish in the Overlapping Multi-fish Image for Freshness Detection
AU - Prasetyo, Eko
AU - Suciati, Nanik
AU - Fatichah, Chastine
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The fish freshness detection application assists the public in determining the freshness of fish purchased at the market. The application operates two principal tasks: detecting body parts' regions of interest (ROI) and classifying freshness. For ROI detection, the You Only Look Once (Yolo) method detects intact fish and their parts, such as heads and tails. Then, a Convolutional Neural Network classifies them for freshness. However, the input image for Yolo may contain fish with arbitrary placement resulting in overlapped and redundant detected parts. Hence, an algorithm to select the appropriate head and tail of an intact fish from the detected parts is required to correctly aggregate the freshness classes of all fish in the image. This study proposes a head and tail selection algorithm using two principal components: the head-tail distance and the intersection over the fish part. The experimental results on 20 overlapping fish images show that the algorithm selects heads and tails with an accuracy of 84.21%. The best weights for both components are 0.6-0.4 to 0.8-0.2.
AB - The fish freshness detection application assists the public in determining the freshness of fish purchased at the market. The application operates two principal tasks: detecting body parts' regions of interest (ROI) and classifying freshness. For ROI detection, the You Only Look Once (Yolo) method detects intact fish and their parts, such as heads and tails. Then, a Convolutional Neural Network classifies them for freshness. However, the input image for Yolo may contain fish with arbitrary placement resulting in overlapped and redundant detected parts. Hence, an algorithm to select the appropriate head and tail of an intact fish from the detected parts is required to correctly aggregate the freshness classes of all fish in the image. This study proposes a head and tail selection algorithm using two principal components: the head-tail distance and the intersection over the fish part. The experimental results on 20 overlapping fish images show that the algorithm selects heads and tails with an accuracy of 84.21%. The best weights for both components are 0.6-0.4 to 0.8-0.2.
KW - distance
KW - fish freshness
KW - head and tail selection
KW - intact fish
KW - intersection over fish part
UR - http://www.scopus.com/inward/record.url?scp=85147092649&partnerID=8YFLogxK
U2 - 10.1109/ITIS57155.2022.10010069
DO - 10.1109/ITIS57155.2022.10010069
M3 - Conference contribution
AN - SCOPUS:85147092649
T3 - Proceeding - IEEE 8th Information Technology International Seminar, ITIS 2022
SP - 195
EP - 199
BT - Proceeding - IEEE 8th Information Technology International Seminar, ITIS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE Information Technology International Seminar, ITIS 2022
Y2 - 19 October 2022 through 21 October 2022
ER -