## 2 Related Work  
A number of researchers have demonstrated the capabilities of several deep learning algorithms in the identification and categorization of trash for waste segregation [5; 21; 35; 36; 37; 38]. These methods encompass CNN and MLP as well as Faster R-CNN [5; 18; 23; 39; 40; 41]. Artificial intelligence (AI) using a deep learning approach has changed today's global market. The approach provides the flexibility to utilize data which can help recognize, remove, and sort objects on a moving conveyer belt [23; 42; 43]. AlexNet, which succeeded the ImageNet large scale visual recognition challenge (ILSVRC) in 2012, is a highly developed architecture that has been demonstrated to function effectively on most picture datasets. It is often used for trash separation although its proficiency is not comparable to that of other pre-trained models. [PERSON] et al. [44] used the OpenCV computer vision library to enhance data and prepare trash photos. TensorFlow was employed as the model training background and a VGG16 convolutional neural network was built using the RELU activation function with the addition of a BN layer to improve the model convergence and recognition accuracy rate [44]. As a result of testing on the test set, the strategy presented exhibited an accuracy of 75.6%. The method proposed was capable of successfully categorizing home rubbish into hazardous garbage, kitchen waste and general garbage. CNN and support vector machine methods were found to be better for trash classification [30]. A ResNet-50 allocation learning model was used due to the limited size of the dataset. The researchers sorted garbage into four categories: glass, paper, plastic, and paper and attained an accuracy of 87% using this method. ResNet-34 is another innovative image categorization model comprised of 34 layers of convolutional neural networks. This is a pre-trained model using the ImageNet dataset, which has 100,000+ images organized into 200 categories [4]. The ResNet-34 network's substructure is a framework that relies on extensive networking [45]. The leftover construction component uses a shortcut link to bypass the convolutional layers, thereby reducing the problem of gradient disappearance or explosion in neural networks, and enabling the design of CNN structures and enhancement of the identification rate [46]. ResNet consists of four layers with similar behavior and one stage of convolution and pooling. Each layer follows the same pattern. The layers achieve a 3 \(\times\) 3 convolution with predetermined feature map dimensions (F) of 64, 128, 256 and 512, skipping the insight after every two convolutions. Consequently, the layer's width (W) and height (H) remain constant [6]. [PERSON] et al. [31] investigated a novel approach to garbage classification for efficient reuse and dumping using a deep learning algorithm. In the Darknet framework, the YOLOv3 approach was utilized to train a self-created dataset. [PERSON] et al. [39] presented an IoT-enabled smart trash classification and managerial device that detects garbage in dustbins using sensor devices; as soon as they are recognized, the waste compounds are separated using sensors and the information is immediately communicated to a cloud database through the IoT. The debris container included is a vital component of the scheme that autonomously sorts rubbish utilizing the Internet of Things and machine learning technologies [33; 47]. The trashcan is tethered to the cloud, which helps with rubbish collection by capturing and transferring data from various points for each bin. Two reiterations of the system are examined, the first of which achieves 75% accuracy in classifying rubbish as wet or dry, and the second of which achieves 90% accuracy in classifying waste into six distinct categories. [PERSON] et al. [40] proposed an IoT system to improve power-saving in trash management in smart cities.