Object detection，a foundational task in computer vision，has attained remarkable performance under well-illuminated conditions with the advancement of deep learning techniques. However，its practical deployment is severely impeded by low-light environments，which are ubiquitous in real-world scenarios including nighttime surveillance，autonomous driving during dusk，and low-illumination indoor monitoring. Traditional object detection methods exhibit substantial performance degradation under low-light conditions，primarily stemming from three core challenges： excessive image noise induced by insufficient photon capture of imaging sensors，reduced contrast between foreground objects and backgrounds that obscures object contours，and severe loss of fine-grained details （e.g.，texture and edge information），which are all critical for accurate object localization and classification. Meanwhile，the development of low-light object detection techniques is further constrained by the scarcity of high-quality annotated low-light datasets. Annotating low-light images demands professional expertise to distinguish genuine object features from noise interference，resulting in high annotation costs and limited dataset scales. By contrast，large-scale natural image datasets （e.g.，COCO and ImageNet） with high-quality annotations are readily accessible，encompassing diverse object categories and scene variations. Consequently，this study aims to address the performance degradation of traditional object detection methods in low-light environments，alleviate the bottleneck posed by scarce high-quality annotated low-light images，and fully exploit the value of large-scale natural image datasets to provide efficient and reliable training support for low-light object detection.

An innovative low-light object detection framework，which adopts a core training paradigm based on normal-light pretrained models to maximize the utilization of existing high-quality natural image resources，is proposed to achieve the aforementioned objectives. The framework consists of two key modules designed to address domain differences and small/distant object detection challenges，respectively. First，to bridge the substantial domain gap between normal-light and low-light images，characterized by distinct illumination distributions and feature representations，an illumination-invariant feature alignment （IIFA） module is developed. This module implements cross-domain feature fusion through a twofold strategy： global illumination feature alignment and illumination-independent feature extraction. The global illumination alignment component normalizes the illumination statistics （e.g.，brightness and contrast） of normal-light and low-light features to a unified space，reducing the impact of illumination intensity differences. The illumination-independent feature extraction component employs an attention mechanism to focus on object structural features （e.g.，shape and contour） that are less affected by illumination changes，while suppressing illumination-related noise features. Additionally，an adaptive sample priority processing mechanism is embedded in the IIFA module to adjust the weight of training samples based on their domain adaptation difficulty dynamically. Samples with larger domain distribution differences （e.g.，low-light images with extreme noise） are assigned higher weights during training，enabling the model to prioritize learning from challenging samples and adapt more effectively to interdomain distribution differences. Second，aiming at the prominent challenge of detecting small or distant objects in low-light environments where degraded details and low contrast make it difficult for the model to capture discriminative features，a two-stage feature enhancement （TSFE） module is proposed. The first stage of the module performs multiscale feature fusion to aggregate low-level detail features and high-level semantic features of small/distant objects，compensating for the detail loss caused by low illumination. The second stage applies a local feature reinforcement attention mechanism to highlight the key feature regions of small/distant objects （e.g.，object heads and limbs for pedestrian detection） while suppressing background noise. This two-stage enhancement strategy ensures that the model can accurately locate and identify small or distant objects even in low-light conditions by sequentially enhancing feature completeness and discriminability.

Comprehensive experiments are conducted on the ExDark dataset，a widely recognized low-light object detection benchmark that contains 8 856 low-light images covering 12 common object categories （e.g.，person，car，and bicycle） and various low-light scenarios （e.g.，night streets and dim rooms）. The proposed model is compared with two mainstream low-light object detection methods： direct training on low-light datasets and normal-light pretraining followed by low-light fine-tuning. The evaluation metrics include mean average precision （mAP） for detection accuracy，precision （P） and recall （R） for category-specific performance，and frames per second （FPS） for detection efficiency. Experimental results demonstrate that the proposed model outperforms the comparison methods in all evaluation metrics，with particularly outstanding performance in small object detection. Specific metrics are as follows： In cross-domain adaptation scenarios （using normal-light pretrained models without fine-tuning on low-light data），the model achieves an mAP of 65.74% and an FPS of 57.03 frame/s，outperforming the direct low-light training method and maintaining comparable efficiency. When fine-tuned on the ExDark dataset，the proposed model achieves an mAP of 66.83%，a precision of 83.73%，and a recall of 54.78%，while maintaining an FPS of 48.81 frame/s，verifying the effectiveness of the IIFA and TSFE modules. The FPS of 48.81 also meets the real-time detection requirements of practical applications such as surveillance and autonomous driving.

This framework effectively bridges cross-domain differences between normal-light and low-light images，enhances the detection capability for small and distant objects，and remarkably improves overall low-light object detection performance，providing an efficient solution to low-light detection challenges.