Animal image recognition system based on Yolov8-AFPN

Object detection technology has been widely applied in the conservation and monitoring of wildlife in recent years. However, due to the complexity of field environments, the collected images of wild animals often suffer from severe lighting effects, partial blurriness, and significant environmental differences. These issues greatly impact the efficiency and accuracy of object recognition. To overcome these challenges, this paper employs Gaussian filtering as an image enhancement technique and introduces an Asymptotic Feature Pyramid Network (AFPN) to improve the YOLOv8 model. The AFPN extracts features from the bottom up and uses adaptive spatial fusion operations to optimize the feature fusion process, significantly enhancing the detection performance of animal image recognition models.