FEDM: a convolutional neural network based fertilised egg detection model

ABSTRACT

1. The production of goose eggs holds significant economic value on a global scale and the quality of fertilised eggs is crucial for the successful hatching and sustained development of the poultry industry. Developing a low-cost fertilised egg identification system that is suitable for large-scale testing is of great significance. However, existing methods are expensive and have high environmental detection requirements, which limit their promotion.

2. To address this issue, an improved object detection model called FEDM based on YOLOv5 is proposed, which has been shown to be outstanding among nine models. The main network of YOLOv5 is enhanced with the SENet attention mechanism to improve the feature selection capability. The C3_DCNv3 is introduced to enhance the detection ability of blood vessels in the fertilised eggs. The application of Dyhead significantly improved the representation capacity of the object detection head without any computational overhead. The loss function is replaced with MPDIoU to simplify the calculation process.

3. Experimental results from the augmented dataset showed that the average precision of the FEDM reached 96.7%, which is a 5.5% improvement compared to the YOLOv5s model. FEDM exhibited better detection performance on eggs from different shooting angles than the YOLOv5 algorithm and achieves high detection speed.

4. The FEDM secured significant advancement on the detection rate of the fourth day fertilised egg compared to the YOLOv5 algorithm. Based on this result, savings and space utilisation can be made, which has practical application value.

KEYWORDS:

• Fertilised egg detection
• YOLOv5
• SENet
• C3_DCNv3
• Dyhead
• MPDIoU

Disclosure statement

No potential conflict of interest was reported by the author(s).