TransSIL: A Silhouette Cue-Aware Image Classification Framework for Bird Ecological Monitoring Systems

How to automatically recognize the bird species has caused concerns in ecological systems and intelligent ecological monitoring system due to the increasing threat to bird ecological society and bird species diversity. However, traditional monitoring systems are susceptible to specific challenges when operating in complex environments, such as complex environments, multifarious postures and backlight scenarios. To effectively address these challenges, we present a novel bird ecological intelligent detection system (TransSIL) for fine-grained bird image classification (FBIC) in diverse ecological to learn discriminative features by explicitly incorporating silhouette structural information alongside critical visual cues. Specifically, the approach begins with a silhouette token construction module to estimate the bird silhouette and extract silhouette tokens. Then, a silhouette relationship mining module is developed to fuse visual and silhouette tokens and capture long-range dependencies between them. In addition, to learn bird distinctive features at multiple levels, a critical cues awareness module is embedded within TransSIL. The performance of TransSIL was evaluated on two bird datasets: CUB200-2011 and NABirds. The framework demonstrates significant improvements over existing ecological intelligent surveillance methods. By utilizing silhouette and visual dependencies, we anticipate that our approach will ultimately contribute to the conservation of avian ecological societies.