## 2 Literature Review  
The algorithms have been implemented for solving complex optimization problems in the state-of-the-art of day-to-day life not only on image processing but in other areas as well: From feature selection [5], to self-adoptive evolutionary algorithm for the berth scheduling problem [6]. The objective of this study is tied with object detection studies: a sector of computer vision related to detecting instances conducted in the literature review. The section is structured as follows: The first subsection would go over several studies that used various objects for image identification, and then, the second subsection would present application of convolutional neural network (CNN) methods in other fields. A vision-based system is a method that has been used widely for detecting roadway environment for various objectives such as road safety improvement. Low-overhead collision algorithm was presented, which was able to perform successfully in real time under various weather conditions [7]. In another study, a system is proposed relying on cellular communication to detect probable vehicle to pedestrian crashes [8]. The results showed promising results on potential of safety improvement. The CNN technique has become one of the state-of-the-art techniques for object detection in the field of machine learning. For instance, a study conducted to evaluate the pedestrian localization accuracy with the help of convolutional neural network model [9]. This system displays the driver real-time locations, and also types of hidden road users at traffic intersection. The data collected and transferred to roadside unit consists of a monovision camera streaming videos to a computing unit, which perform analysis to detect objects and measure distances. The CNN was used in another study for roadside vegetation detection [10]. The data contains vegetation in various conditions. Detecting various traffic signs was achieved by the CNN [11]. This analysis conducted on Malaysian traffic sign database with two types of training: Incremental and batch training. The results indicated that the analysis could reach 99% accuracy. The extensive research also has been conducted for evaluation of the CNN in other fields, which the following paragraphs would outline. A study conducted for traffic scene classification with an objective of improvement of the vehicle environment [12]. They highlighted how a limiting size of image descriptors impact the traffic scene performance classification. High resolution images of synthetic aperture radar (SAR) was used [13]. Different pertained CNN models were used as feature extractors in combination with support vector machine (SVM). AlexNet performed better than other considered methods including VGG and GoogleNet. Thyroid ultrasound images were taken including the total of 1037 thyroid ultrasound images, including two categories: Benign and malignant images [14]. The images were resized to 224 \(\times\) 224. Due to limited number of images, a pre-trained CNN learned from ImageNet was also considered. A pre-trained VGG model for transfer learning was compared with feature subset selection and classification, which was based on extracting the features from CNN. The results indicated that proposed method perform better than the transfer learning technique. Two-phase method combining CNNs transfer learning and web data augmentation was proposed [15]. The original data was augmented with the internet images, expanding the training dataset. The method was proposed when the training data is very limited. The web data augmentation was used to add diversity to the datasets with the purpose of generalization ability and alleviating the overfitting. Various transfer learning techniques were implemented on various datasets. For instance, traditional models outperformed the considered transfer learning of Alexnet, VGG-16, ResNet on events dataset. The transfer learning with CNN for abdominal ultrasound images were used [16]. The images were categorized into 11 categories based on their text annotation.