Delving into Cascaded Instability: A Lipschitz Continuity View on Image Restoration and Object Detection Synergy

2025-10-28

AI Summary

Cascading image restoration with object detection suffers from training instability and performance degradation due to functional mismatch—particularly in gradient behavior—between the restoration and detection networks, a challenge尚未 systematically investigated. Method: This paper pioneers a Lipschitz continuity–based modeling framework to characterize this functional misalignment and proposes Lipschitz-Regularized Object Detection (LROD), a co-optimization scheme that unifies the functional smoothness of both tasks during training. We further introduce a Lipschitz regularization strategy that embeds the restoration module into YOLO’s feature learning pipeline, yielding an end-to-end trainable architecture, LR-YOLO, compatible with mainstream YOLO variants. Contribution/Results: Evaluated on haze and low-light detection benchmarks, LR-YOLO achieves average mAP improvements exceeding 3.5%, significantly enhancing cascade stability and optimization smoothness, thereby validating its effectiveness and generalizability.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

To improve detection robustness in adverse conditions (e.g., haze and low light), image restoration is commonly applied as a pre-processing step to enhance image quality for the detector. However, the functional mismatch between restoration and detection networks can introduce instability and hinder effective integration -- an issue that remains underexplored. We revisit this limitation through the lens of Lipschitz continuity, analyzing the functional differences between restoration and detection networks in both the input space and the parameter space. Our analysis shows that restoration networks perform smooth, continuous transformations, while object detectors operate with discontinuous decision boundaries, making them highly sensitive to minor perturbations. This mismatch introduces instability in traditional cascade frameworks, where even imperceptible noise from restoration is amplified during detection, disrupting gradient flow and hindering optimization. To address this, we propose Lipschitz-regularized object detection (LROD), a simple yet effective framework that integrates image restoration directly into the detector's feature learning, harmonizing the Lipschitz continuity of both tasks during training. We implement this framework as Lipschitz-regularized YOLO (LR-YOLO), extending seamlessly to existing YOLO detectors. Extensive experiments on haze and low-light benchmarks demonstrate that LR-YOLO consistently improves detection stability, optimization smoothness, and overall accuracy.

Problem

• Addresses instability from mismatched restoration-detection network functions
• Analyzes Lipschitz continuity differences causing cascaded framework failures
• Proposes integrated framework to harmonize restoration and detection continuity

Innovation

• Lipschitz-regularized object detection framework
• Integrated restoration into detector feature learning
• Harmonized Lipschitz continuity across both tasks