ED-Ged: Nighttime Image Semantic Segmentation Based on Enhanced Detail and Bidirectional Guidance

Semantic segmentation of driving scene images is crucial for autonomous driving. While deep learning technology has significantly improved daytime image semantic segmentation, nighttime images pose challenges due to factors like poor lighting and overexposure, making it difficult to recognize small objects. To address this, we propose an Image Adaptive Enhancement (IAEN) module comprising a parameter predictor (Edip), multiple image processing filters (Mdif), and a Detail Processing Module (DPM). Edip combines image processing filters to predict parameters like exposure and hue, optimizing image quality. We adopt a novel image encoder to enhance parameter prediction accuracy by enabling Edip to handle features at different scales. DPM strengthens overlooked image details, extending the IAEN module’s functionality. After the segmentation network, we integrate a Depth Guided Filter (DGF) to refine segmentation outputs. The entire network is trained end-to-end, with segmentation results guiding parameter prediction optimization, promoting self-learning and network improvement. This lightweight and efficient network architecture is particularly suitable for addressing challenges in nighttime image segmentation. Extensive experiments validate significant performance improvements of our approach on the ACDC-night and Nightcity datasets.