Research overview: MegaDetector and animal-versus-empty image filtering

MegaDetector is a widely used, freely available object-detection model that locates animals, people, and vehicles in camera-trap images regardless of species, primarily to filter out the empty frames that often make up the majority of captures triggered by wind or vegetation. This overview surveys how MegaDetector works, why detecting that something is present generalizes far better across new sites than naming the species, and how practitioners chain it with species classifiers and review tools into efficient processing pipelines. It discusses the enormous labor savings from removing blanks, integration with platforms and desktop tools, and the model's role as community infrastructure. The overview highlights MegaDetector as a pragmatic, high-impact AI tool that solved the field's most universal bottleneck, empty-image overload, making automated camera-trap processing accessible to projects of all sizes.