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Pedestrian detection in oilfield security is a hot research topic. For the low resolution and complex background in image, this paper is based on the Single Shot MultiBox Detector (SSD), proposed an improved pedestrian detection algorithm. The algorithm firstly uses the aggregate channel feature model to preprocess the image or video sequence to obtain the suspected pedestrian area, which greatly reduces the time of single-frame image detection. The basic network VGG-16 is replaced by Resnet-50, which introduces the identity mapping to solve the problem of reducing the detection accuracy when the number of network layers are increased. Finally, the powerful and flexible two-parameter loss function is used to optimize the training deep network and improve the network model generalization ability. Qualitative and quantitative experiments show that the performance of the proposed detection algorithm exceeds the existing detection algorithm, and the detection efficiency is improved while ensuring the accuracy of pedestrian detection.