Improved YOLOv8 Model for Lightweight Pigeon Egg Detection

Abstract

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.

A lightweight fine-grained recognition algorithm based on object detection

“…Han et al (Han YL, Zheng BW, Kong XH et al (2023) Underwater Fish Segmentation Algorithm Based on Improved PSPNet Network. Sensors 23. https://doi.org/10.3390/s23198072) introduced Triple Attention to PSPNet's pyramid module, facilitating cross-dimensional interaction between spatial information to focus on specific positions of fish body features within channels and enhancing clarity around fish edge positions.Jiang et al (Jiang T, Zhou J, Xie BB et al (2024) Improved YOLOv8 Model for Lightweight Pigeon Egg Detection. Animals 14. https://doi.org/10.3390/ani14081226) also made significant contributions: they incorporated an EMA module into the C2f module and replaced the upsampling module in the neck network with Dysample when designing YOLOv8-PG for rapid detection of fragile egg-shaped objects; experimental results demonstrated substantial improvement from these modifications.Furthermore, Jiang et al (Jiang ZJ, Wu BJ, Ma L et al (2024) APM-YOLOv7 for Small-Target Water-Floating Garbage Detection Based on Multi-Scale Feature Adaptive Weighted Fusion.…”