## 1 Introduction  
With the increase in smart video surveillance, facial detection, autonomous vehicles, and plenty of people counting applications, accurate and fast object detection methods are in increasing demand. Such a system involves classifying and recognizing all the objects in the image but localizing each one by drawing the proper bounding box around it [1]. This makes object detection a considerably more difficult process than its conventional computer vision (CV) predecessor, image classification. The current progression in the fields of deep learning (DL), image processing, and CV technologies has changed the thinking about different features of day-to-day lives [2]. The DL method has given a strong foundation for image detection with consistent accuracy [3]. The widespread image classification, the convolution neural networks (CNNs), is stimulated by biological neural networks that are comprised of different neurons and layers in every layer are closely linked to the neurons in the following layer [4]. The advantages of utilizing a CNN that provides independence between prior knowledge, the least amount of effort in design, and feature extraction. CNNs have made great achievements in image classification and recognition [5]. The accuracy and popularity of CNNs for the classification of images have improved because of the large-scale integrated systems for image processing and learning, the vast accessibility of public data of images, and higher-speed GPUs. The concept of smart waste classification using trash and waste images has great potential. Because of fast urbanization, nowadays, cities are facing several problems. One of them is waste management systems, since the amount of waste is directly proportionate to the number of people who live in urban areas. The city administrations and the municipalities utilize conventional waste classification methods that are very slow, manual, costly, and inefficient [6]. Consequently, automated waste management and classification are indispensable for the city that is being urbanized for the improved recycling of waste. The simplification of the waste classification method is needed since the technologies have been rapidly growing and many manual tasks have been decreased by adopting artificial intelligence (AI) methods [7]. Generally, in the Indian context, waste consists of plastic, paper, metal, rubber, textiles, glass, sanitary products, organics, electronics, and electrical, infectious materials (hospital and clinical), and hazardous substances (paint, spray, and chemicals) [8], are widely categorized as biodegradable (BD) and non-biodegradable (NBD) waste, with a corresponding share of 52% and 48% [9]. Effective waste segregation could help in the appropriate recycling and disposal of this waste according to biodegradability. Therefore, the current era dictates the progress of a smart waste segregation method to suggest the abovementioned cause of ecological damage. Consequently, the segregation of waste has received considerable interest from academicians and researchers worldwide [10]. The proper organization and classification of waste into different classes (such as biodegradable, recyclable, organic, harmful, non-biodegradable, and so on.) assists in the appropriate disposal and utilization of waste. For waste segregation, the CV technique might offer an efficient solution to separate out, identify, and classify the waste from the huge dumps of trash and garbage. [PERSON] et al. [11] examine a method for waste segregation for its efficient disposal and recycling through a DL approach. The YOLOv3 algorithm was used from the Darknet neural network for training self-made datasets. The network was trained for six object types (such as glass, cardboard, paper, metal, organic, and plastic waste). Furthermore, for relative analysis, the detection process was implemented by utilizing YOLOv3-tiny for validating the capability of the YOLOv3. [PERSON] et al.