# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 3 System Architecture
### Data Augmentation via CycleGAN  
In supervised deep learning, training an effective detection model typically requires large volumes of labeled data. Data engineers must manually annotate tens of thousands of images to enable the neural network to learn to recognize specific objects. This process is both time-consuming and labor-intensive. The challenge becomes more pronounced when the target objects are rare or appear only under specific environmental conditions, such as adverse weather or nighttime. In such cases, acquiring a sufficiently diverse and representative dataset during the initial stages of development poses significant difficulties. To address the issue of limited data availability, [PERSON] et al. (2014) introduced generative adversarial networks (GANs) in 2014. A GAN consists of two components: a generator and a discriminator. The generator attempts to create synthetic images that are indistinguishable from real ones, while the discriminator tries to differentiate between real and generated images. Through this adversarial learning process, the generator progressively learns to produce increasingly realistic images. This technique offers a powerful solution to data scarcity by enabling the generation of synthetic training data that mimic real-world samples.  
In 2017, [PERSON] et al. (2017) proposed Cycle-Consistent Adversarial Network (CycleGAN), a specialized form of GAN designed for unpaired image-to-image translation. Unlike conventional GANs that require paired training data (e.g., the same scene captured in two different conditions), CycleGAN only requires sets of images from two different domains. This significantly lowers the barrier to training data preparation as paired samples--such as summer and winter views of the same scene, or visually similar rebras and horses--are often difficult to obtain. CycleGAN extends the GAN architecture by introducing cycle consistency, training two generators and two discriminators simultaneously. The first generator learns to map images from domain A to domain B, while the second generator maps from domain B back to domain A. Cycle consistency ensures that, when an image is translated to the other domain and then translated back, the final output remains consistent with the original input. This bidirectional mapping enforces structural fidelity and enhances the quality of the translated images.  
CycleGAN has been widely adopted in various domains for its ability to generate realistic images under constrained-data conditions. For instance, [PERSON] et al. [31] applied CycleGAN to convert between visible-light and near-infrared facial images, achieving a facial recognition accuracy of 96.5%. [PERSON] et al. [32] used CycleGAN to generate synthetic foggy images from clear remote sensing imagery in order to train a defogging model based on visual features rather than physical simulations. [PERSON] et al. [33] introduced an environment-aware CycleGAN for synthesizing rare or missing typhoon-intensity features using unpaired satellite image sequences. Their results demonstrated that the model could effectively generate samples from previously unseen categories, improving predictive performance.  
These studies highlight the effectiveness of CycleGAN in augmenting datasets under limited or specialized conditions. Building on this foundation, we adopt CycleGAN as the image generation module in our proposed system. Specifically, it is used to generate pedestrian-crossing images under visually challenging scenarios--such as nighttime, rain, and direct glare from sunlight or artificial high beams--that are critical for enhancing the robustness and accuracy of the pedestrian-crossing detection model.