Wildlife monitoring is essential for wildlife conservation and ecosystem maintenance, and wildlife detection and identification is the core technology to achieve monitoring. In recent years, with the rapid development and widespread application of computer vision technology, image-based non-contact methods have attracted extensive attention in the field of wildlife monitoring, and researchers have proposed various methods to solve different problems in this field. However, the complexity of wild environment still poses challenges for accurate detection and identification of wildlife. In order to promote research in this field, the existing image-based wildlife monitoring methods are reviewed in this study, which mainly include three sections: wildlife image acquisition methods, wildlife image preprocessing methods, and wildlife detection and recognition algorithms. These methods are discussed and classified according to the different processing mechanisms of image datasets and wildlife detection and recognition algorithms. Finally, the research hotspots and existing problems of wildlife monitoring based on deep learning are analyzed and summarized, and the prospect for future research priorities is proposed in the study.