## 1 Introduction
[12] employed two deep 3D customized mixed link network (CMixNet) frameworks for lung nodule classification and recognition, correspondingly. Nodule classifications were implemented by using a GBM system on the learned features from the 3D CMixNet framework. Nodule detection was implemented by using fast RCNN on effectively-learned features from U-Net and CMixNet, such as the encoder-decoder framework. [PERSON] et al. [13] presented two-phase DL classifiers to differentiate flowers of a wide-ranging species. Firstly, the flower region is segmented automatically to permit localization of the minimal bounding box around it. The presented method is modeled as a binary classification in a full convolution network architecture. Next, a strong CNN classification is constructed to differentiate the variety of flowers. [PERSON] et al. [14] developed three post-processing methods to increase the benchmark Fast RCNN based on previous knowledge. Firstly, a filtering method is created to remove over-lapping boxes identified by Fast RCNN related to a similar tooth. Then, a NN system is performed to detect missing teeth. At last, a rule-based model based on a teeth number scheme is projected for matching labels of identified teeth boxes to alter outcomes that violate some intuitive rules. [PERSON] et al. [15] presented a strong method using DNN for automatically classifying trash that is employed in smart waste sorter machines. First, it collects the VN-trash data set, which comprises 5904 images belonging to the following three distinct groups: medical, organic, and inorganic wastes from Vietnam. Then, we developed a DNN system for trash classification called DNN-TC, which is an enhancement of ResNeXt to optimize the performance of prediction. [PERSON] et al. [16] introduced a method called \"double fusion\" that optimally integrates various DL methods using score-level fusion and feature models. The double fusion system guarantees an enhanced contribution of the deep model by, initially, integrating the capability of earlier and latter fusion systems and a score-level fusion of the classification result attained by earlier and latter fusion models. [PERSON] et al. [17] designed a smart waste management method with LoRa transmission and a TensorFlow-based DL method. The presented method transmits the sensor data and Tensorflow implements real-time object classification and detection. The bin contains a number of chambers for segregating the waste involving plastic, metal, paper, and common waste compartments that are managed by the servo motor. This study develops a novel deep learning-based small object detection and classification model for garbage waste management (DLSODC-GWM) technique. The proposed DLSODC-GWM involves the design of an arithmetic optimization algorithm (AOA) with an improved RefineDet (IRD) model for an effectual object detection process where the hyperparameters of the IRD model are optimally chosen by the AOA. In addition, the functional link neural network (FLNN) model is applied for the classification of waste objects into multiple classes. In order to demonstrate the significant performance of the DLSODC-GWM approach, a wide-ranging simulation analysis is carried out on benchmark datasets.