Detection of threat objects concealed in passenger clothing and baggage poses a challenge to aviation security. At present, the detection technology is capable of detecting the presence of threats from the scanned images yet requires the involvement of human in determining what type of threat and where it is located. Deep learning-based object detection technique has not been successfully implemented to detect threats in the security screening processes. This research aims to improve the accuracy and the processing time of threat detection in security screening. Enhanced faster region-based convolutional neural network (faster R-CNN) with improved region proposals is used for better threat localization. The proposed system consists of an improved region proposal network that outputs object’s region proposals with an object score to the detector module to accurately locate the threat in the human body. Furthermore, this system uses a modified loss function that strengthens the classification loss. Results obtained by the proposed model show a 15% improvement in object localization. Therefore, the enhanced faster R-CNN achieves an overall detection accuracy of 0.27 in terms of average precision and reduces the processing time by 0.19 s. The results obtained by the enhanced faster R-CNN for detection accuracy are superior to the state-of-the-art system. Also, this model focuses on localizing the threat and identifying its type, which makes the model suitable for threat detection security screening. Besides, the research also addresses the time consumption issue in detecting the threat object.