The efficiency of pig body temperature measurement based on thermal infrared technology is low in the process of large-scale pig breeding. Temperature detection method in herd of pigs based on improved YOLO v7 was proposed, and an automatic pig head detection model was constructed. The VoV-GSCSP structure was introduced at the Head layer to reduce the complexity of the network structure. The content-aware reassembly of features (CARAFE) was used to replace the original up-sampling operator of the model to improve the quality of the feature map after zooming in, and strengthen the effective features in the head region of the pig;the receptive field enhancement module (RFE) was introduced to enhance the extraction capability of the feature pyramid on the head region of the pig. RFE was applied to enhance the extraction capability of the feature pyramid for the head region of pigs. The improved YOLO v7 algorithm had a detection accuracy of 87.9%, recall rate of 92.5%, and mean average precision (mAP) of 94.7% for the pig head. Compared with the original YOLO v7, the accuracy was increased by 3.6 percentage points, the recall was increased by 7.0 percentage points, and the mAP was increased by 3.6 percentage points. The average absolute error of temperature extraction of this method was only 0.16℃, and the detection speed was 222 frames/s, which realized the real-time accurate detection of body temperature of group pigs. Comprehensive results of the above experiments showed that the method can automatically localize the head region of pigs, meet the requirements of high efficiency and high precision for the determination of body temperature of pigs, and provide effective technical support for the automatic detection of body temperature in herd of pigs.