3FL-Net: An Efficient Approach for Improving Performance of Lightweight Detectors in Rainy Weather Conditions

Abstract

Numerous lightweight detection models have been presented in recent years, yet these detectors are inclined to develop for operating under normal weather conditions without adequate studies for rainy conditions. This is one of the causes leads drastically performance degradation of object detectors due to the decrease in visibility. To address above insufficiency, we propose a new and effective approach, named 3FL-Net, to elevate the performance of lightweight object detectors in the presence of rain. Our approach fulfills the goal by closely incorporating four subnetworks, namely feature enhancement subnetwork, feature extraction subnetwork, feature adaptation subnetwork, and lightweight detection subnetwork. The lightweight detection subnetwork achieved the accuracy improvement by learning diverse features from the feature enhancement subnetwork and feature extraction subnetwork via the feature adaptation subnetwork. To further drive the development in object detection induced by rain, we introduce a large-scale driving dataset, called iRain. The full iRain consists of 17,950 real-world rain images, which covers most of the driving scenarios and 85,081 instances explaining five prevalent object classes. Experiment results on divergent rain datasets expose that our 3FL-Net considerably improves the performance of lightweight detectors and surpasses that of the combination models between rain removal and object detection methods.