Low-light object detection involves identifying and locating objects in images captured under poor lighting conditions. It plays a significant role in surveillance and security, night pedestrian recognition, and autonomous driving, showcasing broad application prospects. Most existing object detection algorithms and datasets are designed for normal lighting conditions, leading to a significant drop in detection performance when applied to low-light environments. To address this issue, we propose a Low-Light Detection with Low-Light Enhancement (LDWLE) framework. LDWLE is an encoder-decoder architecture where the encoder transforms the raw input data into a compact, abstract representation (encoding), and the decoder gradually generates the target output format from the representation produced by the encoder. Specifically, during training, low-light images are input into the encoder, which produces feature representations that are decoded by two separate decoders: an object detection decoder and a low-light image enhancement decoder. Both decoders share the same encoder and are trained jointly. Throughout the training process, the two decoders optimize each other, guiding the low-light image enhancement towards improvements that benefit object detection. If the input image is normally lit, it first passes through a low-light image conversion module to be transformed into a low-light image before being fed into the encoder. If the input image is already a low-light image, it is directly input into the encoder. During the testing phase, the model can be evaluated in the same way as a standard object detection algorithm. Compared to existing object detection algorithms, LDWLE can train a low-light robust object detection model using standard, normally lit object detection datasets. Additionally, LDWLE is a versatile training framework that can be implemented on most one-stage object detection algorithms. These algorithms typically consist of three components: the backbone, neck, and head. In this framework, the backbone functions as the encoder, while the neck and head form the object detection decoder. Extensive experiments on the COCO, VOC, and ExDark datasets have demonstrated the effectiveness of LDWLE in low-light object detection. In quantitative measurements, it achieves an AP of 25.5 and 38.4 on the synthetic datasets COCO-d and VOC-d, respectively, and achieves the best AP of 30.5 on the real-world dataset ExDark. In qualitative measurements, LDWLE can accurately detect most objects on both public real-world low-light datasets and self-collected ones, demonstrating strong adaptability to varying lighting conditions and multi-scale objects.

弱光物体检测涉及在光线不足的条件下拍摄的图像中识别和定位物体。它在监控和安防、夜间行人识别和自动驾驶方面发挥着重要作用，展现了广阔的应用前景。大多数现有的对象检测算法和数据集都是为正常照明条件设计的，因此当应用于弱光环境时，检测性能会显著下降。为了解决这个问题，我们提出了一个具有弱光增强的弱光检测 （LDWLE） 框架。LDWLE 是一种编码器-解码器架构，其中编码器将原始输入数据转换为紧凑、抽象的表示（编码），解码器从编码器生成的表示中逐渐生成目标输出格式。具体来说，在训练过程中，低光图像被输入到编码器中，编码器产生特征表示，这些特征表示由两个单独的解码器解码：对象检测解码器和低光图像增强解码器。两个解码器共享相同的编码器并接受联合训练。在整个训练过程中，两个解码器相互优化，引导低光图像增强朝着有利于目标检测的方向改进。如果输入图像正常亮起，则首先通过微光图像转换模块转换为微光图像，然后再馈入编码器。如果输入图像已经是低光图像，则将其直接输入到编码器中。在测试阶段，可以采用与标准对象检测算法相同的方式评估模型。 与现有的对象检测算法相比，LDWLE 可以使用标准的常光对象检测数据集来训练低光鲁棒对象检测模型。此外，LDWLE 是一个多功能的训练框架，可以在大多数单阶段对象检测算法上实现。这些算法通常由三个部分组成：backbone、neck 和 head。在这个框架中，主干充当编码器，而颈部和头部构成对象检测解码器。对 COCO、VOC 和 ExDark 数据集的广泛实验证明了 LDWLE 在弱光物体检测中的有效性。在定量测量中，它在合成数据集 COCO-d 和 VOC-d 上分别实现了 25.5 和 38.4 的 AP，在真实数据集 ExDark 上实现了 30.5 的最佳 AP。在定性测量中，LDWLE 可以准确检测公共现实世界低光数据集和自采集数据集上的大多数物体，表现出对不同照明条件和多尺度物体的强大适应性。