Efficient counting and localizing objects of interest for agricultural automation

Ying, Ang Lay; Nisar, Humaira; Voon, Yap Vooi

doi:10.1109/tencon.2017.8228387

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…In 2017, Sunardi et al [9] applied smartphones, thermal imaging cameras, and MATLAB for poultry egg recognition, achieving a recognition accuracy of 100% [9]. In the same year, Ang et al proposed a method combining robots to statistically count and collect eggs in free-range chicken farms, with a positioning error within 2 cm [10]. In 2018, Ab Nasir A et al designed an automatic egg-grading system with positioning and recognition accuracies exceeding 95% [11].…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv8 Model for Lightweight Pigeon Egg Detection

Jiang,

Zhou,

Xie

et al. 2024

Animals

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In response to the high breakage rate of pigeon eggs and the significant labor costs associated with egg-producing pigeon farming, this study proposes an improved YOLOv8-PG (real versus fake pigeon egg detection) model based on YOLOv8n. Specifically, the Bottleneck in the C2f module of the YOLOv8n backbone network and neck network are replaced with Fasternet-EMA Block and Fasternet Block, respectively. The Fasternet Block is designed based on PConv (Partial Convolution) to reduce model parameter count and computational load efficiently. Furthermore, the incorporation of the EMA (Efficient Multi-scale Attention) mechanism helps mitigate interference from complex environments on pigeon-egg feature-extraction capabilities. Additionally, Dysample, an ultra-lightweight and effective upsampler, is introduced into the neck network to further enhance performance with lower computational overhead. Finally, the EXPMA (exponential moving average) concept is employed to optimize the SlideLoss and propose the EMASlideLoss classification loss function, addressing the issue of imbalanced data samples and enhancing the model’s robustness. The experimental results showed that the F1-score, mAP50-95, and mAP75 of YOLOv8-PG increased by 0.76%, 1.56%, and 4.45%, respectively, compared with the baseline YOLOv8n model. Moreover, the model’s parameter count and computational load are reduced by 24.69% and 22.89%, respectively. Compared to detection models such as Faster R-CNN, YOLOv5s, YOLOv7, and YOLOv8s, YOLOv8-PG exhibits superior performance. Additionally, the reduction in parameter count and computational load contributes to lowering the model deployment costs and facilitates its implementation on mobile robotic platforms.

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