An improved algorithm has been proposed to address the challenges encountered in object detection using visible and thermal infrared images. These challenges include the diversity of object detection perspectives, deformation of the object, occlusion, illumination, and detection of small objects. The proposed algorithm introduces the concept of contrastive learning into the YOLOv5 object detection network. To extract image features for contrastive loss calculation, object and background image regions are randomly cropped from image samples. The contrastive loss is then integrated into the YOLOv5 network, and the combined loss function of both object detection and contrastive learning is used to optimize the network parameters. By utilizing the strategy of contrastive learning, the distinction between the background and the object in the feature space is improved, leading to enhanced object detection performance of the YOLOv5 network. The proposed algorithm has shown pleasing detection results in both visible and thermal infrared images.
Images taken under low-light conditions tend to suffer from poor visibility, which can decrease image quality and even reduce the performance of the downstream tasks. It is hard for a CNN-based method to learn generalized features that can recover normal images from the ones under various unknow low-light conditions. In this paper, we propose to incorporate the contrastive learning into an illumination correction network to learn abstract representations to distinguish various lowlight conditions in the representation space, with the purpose of enhancing the generalizability of the network. Considering that light conditions can change the frequency components of the images, the representations are learned and compared in both spatial and frequency domains to make full advantage of the contrastive learning. The proposed method is evaluated on LOL and LOL-V2 datasets, the results show that the proposed method achieves better qualitative and quantitative results compared with other state-of-the-arts.
Images taken under low light conditions tend to suffer from poor visibility, which can decrease image quality and even reduce the performance of downstream tasks. It is hard for a CNN-based method to learn generalized features that can recover normal images from the ones under various unknown low light conditions. We propose to incorporate the contrastive learning into an illumination correction network to learn abstract representations to distinguish various low light conditions in the representation space, with the purpose of enhancing the generalizability of the network. Considering that light conditions can change the frequency components of the images, the representations are learned and compared in both spatial and frequency domains to make full advantage of the contrastive learning. Additionally, a grayscale self-weight perception method is used to preproccess the images to reduce the complexity of the model in coping with the uneven distribution of image illumination. The proposed method is evaluated on LOL and LOL-V2 datasets, and the results show that the proposed method achieves better qualitative and quantitative results compared with other state-of-the-art methods.
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