Object detection technology enhances the decision-making efficiency of intelligent systems through real-time localization and accurate classification, playing a vital role in traffic monitoring, industrial inspection, and medical image analysis. Heterogeneous image fusion-based detection integrates multimodal data such as Synthetic Aperture Radar(SAR), infrared, and visible images, effectively overcoming single-modality limitations and providing more comprehensive feature representations, thereby achieving superior detection performance. This paper systematically investigates heterogeneous image fusion strategies for object detection and their adaptation to various detection frameworks. Specifically, it introduces fusion strategies at the point, region, feature, and multi-level, analyzes their technical characteristics, and examines their application within single-stage and two-stage detection frameworks along with their respective advantages and limitations. Furthermore, it synthesizes state-of-the-art approaches for joint optimization of fusion and detection. Finally, commonly used datasets and evaluation metrics are presented, along with promising application prospects in intelligent transportation, industrial inspection, and smart healthcare.