In order to solve the problems of difficulties and low detection accuracy caused by the small sample size and variable target scale of wildlife datasets， a few-shot wildlife detection （MS-FSWD） algorithm based on multi-scale context extraction was proposed. Firstly， the multi-scale context extraction module was used to enhance the perception ability of the model for wildlife at different scales and improve the detection performance. Secondly， Res2Net was introduced as a strong classification network of the prototype calibration module to correct the class scores output by the classifier. Then， the shuffle attention mechanism was added to the RPN to enhance the feature map of the target region and weaken the background information.Finally， using the Balanced L1 Loss as the localization loss function improves the target positioning performance.Experimental results show that compared with the DeFRCN algorithm， MS-FSWD improves the novel class AP50 by 9.9% and 6.6% respectively in the 1-shot and 3-shot detection tasks on the few-shot wildlife dataset FSWA. On the public dataset PASCAL VOC， MS-FSWD is increased by up to 12.6%. Compared with the VFA algorithm， in the 10-shot task of the PASCAL VOC dataset Novel Set 3， the novel class AP50 is increased by 3.3%.

few-shot object detection;wildlife detection;transfer learning;multi-scale context extraction;attention mechanism