Object detection is key for wildlife monitoring, but complex habitats reduce its effectiveness. Using image segmentation to remove redundant environmental parts can enhance detection accuracy. Therefore, we introduce the Segment Anything Model (SAM), which has strong image segmentation capabilities and performs well in various tasks. Additionally, we use the you only look once (YOLO) algorithm, known for its fast and accurate object detection, to meet our needs for intelligent wildlife monitoring. Considering the rapid and accurate detection capabilities of YOLO and the powerful zero-shot segmentation capabilities of SAM to reduce environmental impact, we propose a method called segment wildlife YOLO (SW-YOLO). The results indicate that SW-YOLO achieves a 7.2% higher mAP50 and an 8.2% higher mAP50-95 compared to YOLO. SW-YOLO will enhance the accuracy of wildlife object detection.