###### Abstract  
Human detection is a special application of object recognition and is considered one of the greatest challenges in computer vision. It is the starting point of a number of applications, including public safety and security surveillance around the world. Human detection technologies have advanced significantly in recent years due to the rapid development of deep learning techniques. Despite recent advances, we still need to adopt the best network-design practices that enable compact sizes, deep designs, and fast training times while maintaining high accuracies. In this article, we propose ReSiNet, a novel compressed convolutional neural network that addresses the issues of size, detection speed, and accuracy. Following SqueezeNet, ReSiNet adopts the fire modules by examining the number of fire modules and their placement within the model to reduce the number of parameters and thus the model size. The residual connections within the fire modules in ReSiNet are interpolated and finely constructed to improve feature propagation and ensure the largest possible information flow in the model, with the goal of further improving the proposed ReSiNet in terms of detection speed and accuracy. The proposed algorithm downsizes the previously popular Tiny-YOLO model and improves the following features: (1) faster detection speed; (2) compact model size; (3) solving the overfitting problems; and (4) superior performance than other lightweight models such as MobileNet and SqueezeNet in terms of mAP. The proposed model was trained and tested using MS COCO and Pascal VOC datasets. The resulting ReSiNet model is 10.7 MB in size (almost five times smaller than Tiny-YOLO), but it achieves an mAP of 63.74% on PASCAL VOC and 27.3% on MS COCO datasets using Tesla k80 GPU. computer vision; object detection; human detection; convolutional neural networks +
Footnote †: journal: _Sciences_  
object detection includes the identification of specific details in an image, and localizing its coordinates is considered to be a problem in vision technology. Identifying objects is not the only task that requires performance but categorizing them accordingly across various classes in an appropriate manner is also required [2]. A classic example of this includes visual object detection [2]. Figure 1 illustrates the basic operation of a machine learning (ML) model for detecting objects. For example, consider the goal of classifying three dissimilar objects: a bird, a human being, and a lion. Initially, training images are collected with labeled data in preparation for training an ML framework. Secondly, the desired features are extracted and then added to the classifier's architecture. Certain features can be best expressed by utilizing various object characteristics that include colors, corners, edges, ridges, and regions or blobs [3]. The success achieved from training is directly proportional to several factors that include feature extraction, classifier selection, and the training procedure. The first task is important as it not only enhances the accuracy of trained networks but also eliminates redundant features in the image. It involves reducing the dimensionality of data by extracting redundant information, which in turn improves the quality of inference while simultaneously improving the training rate. An ideological view is to expect features to be invariable in the control of dynamic and illuminated conditions while possessing the capability to cope with any randomized variations during either scaling or rotational motions. Features are appended to the training framework after all feasible features from the image samples have been extracted. They are then supplied to an appropriate sort of classifier based on accuracy and speed. Some normally used classifiers exist, which include the Support Vector Machines (SVM), Nearest Neighbor (NN), Random Forest (RF), and Decision Tree (DT).