## 1 Introduction  
The Internet of Things (IoT) is a large network comprising devices connected to it [1]. IoT is expected to embed various technologies into human daily life, such as using Edge computing, Fog computing and Cloud computing for data processing [2, 3]. The IoT Trend has created a sub-segment of the IoT market known as the Industrial Internet of Things (IIoT) [4, 5], and computer vision has been applied in the IIoT to coordinate industrial production [6], such as the application of target recognition in remote sensing images [7], intelligent remote monitoring of production line [8], etc. IoT uses various sensors to generate and collect data [9]. Due to the inherent problems of sensors and/or environmental conditions, sensors may be unreliable [10]. Therefore, the IoT data have the characteristics of massive, complex, not stuck, and deep learning is suitable for processing this type of data [11, 12]. Object detection is one of the most important and widely discussed topics in computer and machine vision [13, 14]. In addition, object detection is the first step to extract the most informative pixels from the video sequence captured by the vision sensor of the Internet of Things [15]. With the development of machine vision, object detection technology based on deep learning, characterized by a simple and efficient network structure, has surpassed the traditional algorithm, greatly improves accuracy and efficiency, and gradually has become the mainstream algorithm at present. The type of objects that can be detected by the detection method is determined by the picture dataset in which the CNN was trained. Object detection will serve as a foundation for further picture and video operations, such as finding the object's location in the image, determining the object's analogy, and so on. The CNN-based method has an edge in detecting objects in photos, owing to CNN's ability to extract a large amount of semantic information from images. Single-class object detection, multi-class or universal object detection, static image object detection, video object detection, and so on are all examples of object detection. Object detection is divided into three stages: categorization, detection, and segmentation. The range, amount, scale modification, and external setting environment disturbance, however, make the object detection task difficult [16; 17; 18]. Many researchers have dedicated themselves to this field of research in order to overcome these challenges, and they have had a number of triumphs. However, the present deep learning-based object detection algorithm is not friendly to small objects. The most important reason is that the capacity of extracted image features of little objects in an image is significantly smaller than that of massive items in the method of feature extraction, causing the CNN to pay more attention to the large objects in the image. In CNN, the loss of image information is mainly caused by downsampling. The explanations for substantial information loss are stated as follows in the approach of picture knowledge within the convolutional neural network:  
* Too large downsampling rate. Assume that the current small object size is 15 \(\times\) 15. In general object detection, the convolution downsampling rate is 16, so in the feature map, a too large downsampling rate makes small objects unable to occupy even one pixel. * Too large receptive field. In the convolution network, the receptive field of the feature points on the feature map is much larger than the downsampling rate, resulting in fewer features occupied by small objects in a point on the feature map, which will contain a large number of features of the surrounding area, thus affecting its detection results. * Contradiction between semantics and space. The backbones of current detection algorithms are mostly from top to bottom, and the deep and shallow feature maps do not achieve a better balance between semantics and space. For example, the YOLO algorithm can meet the real-time requirements, but the detection accuracy is low.