# Deep-Learning Based Algorithm for Detecting Targets in Infrared Images
## 1 Introduction  
Infrared image target detection identifies and labels each target class from an infrared image containing multiple targets. Infrared images consist of information about the thermal radiation emitted by the target and are not susceptible to environmental influences. Therefore, infrared images have advantages over visible images in low-visibility environments, such as night scenes, haze, rain, snow, and dust. In recent years, IoT technologies such as nighttime intrusion warning systems have cited infrared images based on this advantage [1]. Target detection algorithms, in general, can be divided into two categories: traditional target detection algorithms based on image processing and machine learning and new target detection algorithms based on deep learning. Traditional infrared image target detection algorithms include edge detection, module matching, Hough transform, etc. Some target detection algorithms use edges, contours, and textures for target detection. [PERSON] et al. proposed using gradient direction histograms to detect HOG features of pedestrians [2]. They divided the image and obtained the directional histogram of the gradient edges of each pixel point in each region. The combined directional histogram was used as a feature representation for each area. [PERSON] et al. proposed using Haar wavelet features for target detection, calculating the pixel values in adjacent rectangles obtained from the detection window and their differences and then using the differences to classify each region in the image [3]. [PERSON] et al. proposed to detect pedestrians using Edgelet features and obtained high target detection performance [4]. Traditional target detection algorithms extract features manually for images. These features rely on a priori knowledge and have limited expressiveness, limiting the accuracy of target detection algorithms. In recent years, with the rapid development of deep learning, many deep-learning algorithms have been applied to the field of computer vision. Deep-learning-based target detection algorithms have been proposed one after another. Compared with traditionaltarget detection algorithms that use manual feature extraction, deep-learning-based target detection algorithms can self-extract features, which do not require a priori knowledge and have the more expressive power of the extracted features. This is more beneficial to improve the performance of target detection models. In 2014, [PERSON] applied the regional convolutional neural network to target detection and proposed the R-CNN model [5]. This model is an essential milestone in deep-learning-based target detection algorithms. R-CNN first uses the Selective Search method to extract about 2000 candidate regions, then uses CNN to remove features from the stretched candidate regions, and finally uses support vector machine SVM to classify these features and box regression. In 2015, [PERSON] proposed a faster Fast R-CNN based on R-CNN [6]. Unlike the computational process of R-CNN, Fast R-CNN first convolves the whole image to get the feature map and then combines the two steps of candidate region classification and frame regression for training so that the computation speed is faster. Neither R-CNN nor Fast R-CNN solves the problem of relying on the selective search algorithm in the candidate region generation phase, which causes a very time-consuming pain, so [PERSON] et al. proposed the Faster R-CNN model. Faster R-CNN introduces a Region Proposal Network (RPN), which extracts candidate regions directly on the feature map output from the convolutional neural network, significantly improving the detection speed of the target detection model. Then, [PERSON] proposed the ION model based on the Faster R-CNN model [7]. This model uses spatial recurrent neural networks to combine contextual features and the output of the features from different convolutional layers and uses them as multiscale features for target detection. The above studies mainly focus on target detection in visible images.