Because of the dark atmosphere created by YOLO v3's poor detection accuracy, the photographs obtained in reality usually considerably improve the difficulty of target detection tasks in extremely low light settings. Under extremely low light conditions, the YOLOv3 model is used to suggest a high-discrimination target detection approach. Target detection is an essential task in the field of computer vision, in which the image is the source of input, but the image obtained in reality may make the target detection task in extremely low light conditions because of the dark environment and poor quality imaging equipment, which increases the difficulty of target detection and affects the accuracy of detection. Therefore, based on the above reasons, this paper proposes a high-discrimination target detection method based on the YOLOv3 model under extremely low light conditions. The low light enhancement algorithm based on dehazing algorithm is improved for restraining the part of the transmission rate less than 0.5 in the pixel, to enhance the contrast of the processed images. The improved low light enhancement algorithm is 5.32% higher based on the evaluation index of target detection than the original images. Coiflet wavelet transform is applied to achieve high-discrimination feature extraction of images and decompose the low-frequency features of image information at different resolutions to obtain high-frequency features in the horizontal, vertical, and diagonal directions. The eventually constructed YOLOv3 network has reduced the negative impact of the detected target on the detection accuracy under the illumination change. The results reveal that the target detection method established in this paper has been enhanced by 40.6% on low light photos when compared to the traditional YOLOv3 model, and it still has considerable advantages in comparison to other benchmark approaches in target detection models.