## 1 Introduction
Several researchers have studied the application of deep models in smoke and fire detection, and various methodologies have been developed to achieve accurate and efficient results [28; 29]. One of the approaches involves using CNN architectures, such as VGG and customized CNNs, to extract fine details and features for fire detection [3]. Attention-based networks have also been used to enhance the accuracy of fire detection, with evaluations performed on custom datasets and compared with state-of-the-art CNN architecture [30]. Other deep-learning-based strategies for fire detection include the attention- and squeeze-customized CNN [31], U-shape network [31], Adam network [32], CNN-SA [33], and Adams predictor-corrector color weights network [34]. Moreover, [PERSON] et al. [35] proposed the ConvNeXtTiny model for fire detection in a real-world environment. They performed their experiments on two benchmarks, the Yar and Foggia datasets, and obtained better performance. A vision transformer-based fire scene classification model is developed in [36]. [PERSON] et al. [37] proposed a lightweight CNN model inspired by the VGG model and achieved promising performance [37]. A modified CNN model with an attention mechanism for effective fire detection is proposed in [3; 9; 38; 39]. Furthermore, [PERSON] et al. [40] proposed an efficient network for small object detection, and some other researchers have created fire localization techniques using advanced detection methodologies that rely on deep learning. For instance, [PERSON] et al. [4] employed the YOLOv5 model with several modifications, such as using focus and stem modules in the backbone, replacing a larger filter in spatial pyramid pooling with a smaller one, and adding a p6 module in the head part. This modification obtained higher performance with lower model complexity. Some other approaches include Fire YOLO [41], RCNN [42], Faster RCNN [43],YOLOv2 [44], YOLO V5 [45], YOLOv4 [46], YOLOX [47], and sparse residual network YOLO [48]. Despite achieving SOTA accuracy, CNN-based models face several challenges. Implementing fire detection systems using complex machine learning models raises concerns regarding their computational complexity, which requires more extended training and testing times. Due to this, their deployment on edge devices is questionable. Furthermore, the precision of these models is currently inadequate for practical implementation, and they have relatively high false alarm rates, necessitating further enhancements. Additionally, these methods rely on traditional weight initialization methods, which can lead to vanishing gradient problems and increased computational costs. To tackle these challenges, we developed a lightweight CNN-driven model for fire detection that delivers superior accuracy and minimizes false alarm rates. Our approach employs a backbone architecture based on EfficientNet, combined with stacked, encoded layers to improve accuracy and reduce false-positive rates. We chose EfficientNet as the backbone architecture because it incorporates compound scaling techniques that allow consistent adjustments to the network's depth, width, and resolution. This approach ensures optimal performance and efficiency compared to other CNN architectures that adjust these factors manually. Our research makes the following significant contributions:  
* Introduction of E-EFNet: The primary contribution of this research lies in the design of E-EFNet, a novel fire detection model. E-EFNet is designed to excel in fire recognition while reducing the occurrence of false alarms. This model leverages the lightweight EfficientNetB0 as a foundational feature extractor, augmented by a series of stacked autoencoders for refined feature extraction before the final classification phase. * Addressing False Alarms: E-EFNet's contribution lies in reducing false alarms in fire detection.