Images acquired by computer vision systems under low light conditions have multiple characteristics like high noise, lousy illumination, reflectance, and bad contrast, which make object detection tasks difficult. Much work has been done to enhance images using various pixel manipulation techniques, as well as deep neural networks - some focused on improving the illumination, while some on reducing the noise. Similarly, considerable research has been done in object detection neural network models. In our work, we break down the problem into two phases: 1)First, we explore which image enhancement algorithm is more suited for object detection tasks, where accurate feature retrieval is more important than good image quality. Specifically, we look at basic histogram equalization techniques and unpaired image translation techniques. 2)In the second phase, we explore different object detection models that can be applied to the enhanced image. We conclude by comparing all results, calculating mean average precisions (mAP), and giving some directions for future work.
翻译:计算机视觉系统在低光条件下获得的图像具有多种特征,如高噪音、低光度照明、反射和差差对比,这使得物体探测任务变得困难。我们做了大量工作,利用各种像素操纵技术以及深神经网络来提升图像,其中一些侧重于改进照明,而有些则侧重于减少噪音。同样,对物体探测神经网络模型也进行了大量研究。在我们的工作中,我们把问题分为两个阶段:1)首先,我们探索哪些图像增强算法更适合物体探测任务,而准确的特征检索比良好的图像质量更重要。具体地说,我们研究了基本直方平准技术和非平准图像翻译技术。2)在第二阶段,我们探索了可用于增强图像的不同物体探测模型。我们最后通过比较所有结果,计算平均精度(MAP)和为未来工作提供一些方向。