Research Article Deep Transfer Learning Based Multiway Feature Pyramid Network for Object Detection in Images Parvinder Kaur , 1 Baljit Singh Khehra , 2 and Amar Partap Singh Pharwaha 3 1 Research Scholar, IKG PTU, Jalandhar, Punjab, India 2 Department of CSE, BBSBEC Fatehgarh Sahib, Fatehgarh Sahib, India 3 Department of ECE, SLIET Longowal, Longowal, India Correspondence should be addressed to Baljit Singh Khehra; baljit.singh@bbsbec.ac.in Received 20 January 2021; Revised 23 March 2021; Accepted 3 April 2021; Published 19 April 2021 Academic Editor: Vijay Kumar Copyright©2021ParvinderKauretal.isisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Object detection is being widely used in many fields, and therefore, the demand for more accurate and fast methods for object detection is also increasing. In this paper, we propose a method for object detection in digital images that is more accurate and faster. e proposed model is based on Single-Stage Multibox Detector (SSD) architecture. is method creates many anchor boxes of various aspect ratios based on the backbone network and multiscale feature network and calculates the classes and balances of the anchor boxes to detect objects at various scales. Instead of the VGG16-based deep transfer learning model in SSD, we have used a more efficient base network, i.e., EfficientNet. Detection of objects of different sizes is still an inspiring task. We have used Multiway Feature Pyramid Network (MFPN) to solve this problem. e input to the base network is given to MFPN, and then, the fused features are given to bounding box prediction and class prediction networks. Softer-NMS is applied instead of NMS in SSD to reduce the number of bounding boxes gently. e proposed method is validated on MSCOCO 2017, PASCAL VOC 2007, and PASCAL VOC 2012 datasets and compared to existing state-of-the-art techniques. Our method shows better detection quality in terms of mean Average Precision (mAP). 1. Introduction Object detection is flouted into an extensive room of enterprises, with uses ranging from security to efficacy in the working environments. One very simple application can be locating the lost keys in a messy room. Other applications are surveillance, unmanned vehicles, counting the number of people in a scene, filtering, sa- lacious images on the Internet, detecting abnormalities in scenes such as bombs, real-time vehicle detection in metro cities, machine investigation, image retrieval, face detection, pedestrian detection, activity recognition, human-computer interaction, service robots, and many more [1]. e beginning of the last decade was very lucky for deep learning due to the increased computational speed of GPU and the availability of extremely large datasets that contain millions of labeled data. ese two proved booms to deep learning and object detection, and a series of object detection and localization methods started [2]. Overfeat [3] was proposed by Sermanet et al. in 2014. It used a single convolution neural network to perform classification, detection, and localization of objects in images. It also emphasizes on the concept that avoiding the training of background allows the network to focus on positive classes merely. However, in this method, they were not backpropagating through the whole network. R-CNN (Region with CNN features) [4] wasproposedbyGirshicketal.in2014.Itwasanexcellent achievement in the field of object detection. It combined the concept of region proposal with CNN. Selective search was used to extract 2000 regions from the image, and these regions were called region proposals. Support Vector Machine (SVM) was used for detection of objects. It gave 30 percent better performance over the existing methods. However, still this algorithm takes a large amount of time to train the network. Erhan et al. in 2014 Hindawi Mathematical Problems in Engineering Volume 2021, Article ID 5565561, 13 pages https://doi.org/10.1155/2021/5565561