Object detection algorithm for eggs of Pomacea canaliculata in a paddy field environment

As an invasive species in China, <i>Pomacea canaliculata</i> severely impacts crop quality and yield, necessitating effective monitoring for food security. To address the challenges in detecting its eggs in paddy fields-including feature contamination, stem and leaf occlusion, and dense targets-we propose an enhanced YOLOv8n-based algorithm. The method introduces omni-dimensional dynamic convolution (ODConv) in the backbone network to improve target feature extraction, constructs a Slim-neck structure to optimize feature processing efficiency, and designs a receptive-field attention head (RFAHead) for detection refinement. Experimental results demonstrate that the improved model achieves 3.3% and 4.2% higher [email protected] and [email protected]:0.95 than the original YOLOv8. It outperforms Faster R-CNN, YOLOv3-tiny, YOLOv5, YOLOv6, YOLOv7-tiny, YOLOv9-t, YOLOv10n, and YOLOv11n by 18.2%, 12.4%, 5.2%, 10.8%, 11.6% 5.0%, 3.8%, and 3.4% in [email protected] and 20.6%, 17.5%, 8.1%, 15.6%, 16.1%, 7.0%, 7.7%, and 6.5% in [email protected]:0.95, respectively. Visual analysis confirms enhanced recognition of small and occluded targets through improved feature learning. This model enables accurate and rapid detection of <i>Pomacea</i> eggs in rice fields, offering technical support for invasive species control.