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CLAM MEAT DETECTION ALGORITHM BASED ON IMPROVED YOLOv5s

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Název: CLAM MEAT DETECTION ALGORITHM BASED ON IMPROVED YOLOv5s
Autoři: Xinkai JIAO, Xiangcai ZHANG, Zhongcai WEI, Xianliang WANG, Xiupei CHENG, Pingchuan MA
Zdroj: INMATEH Agricultural Engineering. :609-620
Informace o vydavateli: INMA Bucharest-Romania, 2025.
Rok vydání: 2025
Popis: Intelligent and accurate shelling technology is essential for improving the quality of clam products. To enable the rapid and precise localization of clam meat in Ruditapes philippinarum (with half-shell) on an automated processing line, an improved clam meat detection algorithm - EET-YOLOv5, based on YOLOv5s - is proposed. This algorithm enables real-time detection and localization of clam meat on the production line. It integrates the Efficient Local Attention (ELA) mechanism to enhance target localization, adopts the EIoU loss function to reduce bounding box regression error, and replaces the original detection head with a TSCODE decoupled head to improve detection accuracy. The algorithm achieved a Precision of 93.03%, Recall of 97.03%, and mean Average Precision (mAP) of 93.55%, with a detection speed of 13.3 ms. Compared to YOLOv4, Faster R-CNN, SSD, and the standard YOLOv5 series, EET-YOLOv5 demonstrated superior performance. It was deployed on a test workbench for positioning experiments, achieving an average response time of 1.8 seconds and a positioning success rate of 92.7%, indicating its suitability for automated clam shell-meat separation production lines.
Druh dokumentu: Article
Jazyk: English
ISSN: 2068-2239
2068-4215
DOI: 10.35633/inmateh-76-52
Přístupové číslo: edsair.doi...........0027a6c9bcbb400f9d200f73991e897c
Databáze: OpenAIRE
Popis
Abstrakt:Intelligent and accurate shelling technology is essential for improving the quality of clam products. To enable the rapid and precise localization of clam meat in Ruditapes philippinarum (with half-shell) on an automated processing line, an improved clam meat detection algorithm - EET-YOLOv5, based on YOLOv5s - is proposed. This algorithm enables real-time detection and localization of clam meat on the production line. It integrates the Efficient Local Attention (ELA) mechanism to enhance target localization, adopts the EIoU loss function to reduce bounding box regression error, and replaces the original detection head with a TSCODE decoupled head to improve detection accuracy. The algorithm achieved a Precision of 93.03%, Recall of 97.03%, and mean Average Precision (mAP) of 93.55%, with a detection speed of 13.3 ms. Compared to YOLOv4, Faster R-CNN, SSD, and the standard YOLOv5 series, EET-YOLOv5 demonstrated superior performance. It was deployed on a test workbench for positioning experiments, achieving an average response time of 1.8 seconds and a positioning success rate of 92.7%, indicating its suitability for automated clam shell-meat separation production lines.
ISSN:20682239
20684215
DOI:10.35633/inmateh-76-52