The field of wildlife monitoring and detection is moving towards the development of more efficient and effective systems for detecting and tracking animals in various environments. This is being driven by the need for more accurate and reliable methods for conservation and management of wildlife populations. Recent research has focused on the use of deep learning models, thermal imaging, and multimodal datasets to improve detection accuracy and reduce false positives. Notably, some papers have made significant contributions to this field, including the development of compressed deep learning models for edge devices and the creation of multimodal wildlife monitoring datasets.

Noteworthy papers include: TinyEcoWeedNet, which presents a compressed version of EcoWeedNet for real-time aerial agricultural weed detection, achieving high precision and recall while reducing model size and computations. Real-time Deer Detection and Warning in Connected Vehicles via Thermal Sensing and Deep Learning, which demonstrates exceptional performance in detecting deer using thermal imaging and deep learning, with potential to reduce deer-vehicle collisions. SmartWilds, which introduces a multimodal wildlife monitoring dataset supporting comprehensive environmental monitoring and conservation research. KAMERA, which provides a comprehensive system for multi-camera, multi-spectral synchronization and real-time detection of seals and polar bears, reducing dataset processing time and enabling accurate surveyed area estimates. A Comprehensive Evaluation of YOLO-based Deer Detection Performance on Edge Devices, which evaluates state-of-the-art deep learning models for deer detection and provides a publicly available dataset and benchmarking results.