The existing deep learning-based object detection algorithms encounter various issues during the object detection process in images, such as object viewpoint diversity, object deformation, detection occlusion, illumination variations, and detection of small objects. To address these issues, this paper introduces the concept of contrastive learning into the SSD object detection network and improves the original SSD algorithm. First, by randomly cropping object images and background images from sample images using the method of image cropping, the object image blocks and background image blocks are input into the contrastive learning network for feature extraction and contrastive loss calculation. The supervised learning method is then used to train the SSD network, and the contrastive loss is fed into the SSD network and weighted and summed with the SSD loss value for feedback to optimize the network parameters. Because the contrastive learning concept is introduced into the object detection network, the distinction between the background and object in the feature space is improved. Therefore, the proposed algorithm significantly improves the accuracy of the SSD network for object detection, and obtains satisfactory detection results in both visible and thermal infrared images. In the experiment on the PASCAL VOC2012 dataset, the proposed algorithm shows an increase in the AP50 value by 0.3%, whereas in the case of the LLVIP dataset, the corresponding increase in AP50 value is 0.2%.