Abstract: Traditional object detection methods have limitations in handling complex scenes,especially in low light conditions at night and shaded environments during the day,making it difficult to achieve ideal results.Existing multimodal image fusion techniques tend to emphasize the importance of infrared images in low light scenes,while neglecting the need for a balance between infrared and visible light fusion in complex daytime environments.Therefore,in response to the object detection demand for all-day and multi-scenario environments,this paper proposes a multi-modal fusion object detection method based on feature map classification and GAN.Unlike previous fusion methods that emphasize visual quality of images,this paper focuses on improving the object detection performance of fused images.By using a multi-scale attention mechanism to classify feature maps into saliency and detail feature maps,and optimizing the fusion effect through a generator and saliency and detail discriminators in a cross adversa-rial training network,key information of each modality is captured to meet the detection needs of different scenarios.The experimental results show that the proposed method performs well on TNO,RoadScene,and M3FD datasets,significantly improving the performance of multimodal fusion object detection.

Key words: Multimodal fusion, Object detection, Generate adversarial networks, Attention mechanism