# Buffer-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection
## 2 Related Work
Even though the proposed classifiers surpassed previous methods, image noise still has a powerful impact on the ML classifiers, which restricts classifiers to an average detection score no better than 68%. To boost the average detection score and to reduce the FP rate of CWD, deep learning (DL) frameworks were then employed. One of the main techniques of DL involved the attainment of visible imagery (VI) and MMW image datasets as a pre-requisite to achieve highly precise segmentation [17]. The preliminary deep neural network (DNN) models have an issue of becoming stuck in local optima during optimization in the case of binary images present in MMW imagery datasets. In contrast to DNN models, a convolutional neural network (CNN) has a great tendency to separate useful features from the images automatically by converging the optimizer to a minimum loss function [37]. CNN models manifested promising results for the detection of concealed weapons. Other SOTA CNN-based segmentation architectures were also presented, such as Segnet and Multi-scale Segnet CNN, which enhanced the performance of CWD [38; 39]. Different region-based CNN architectures, such as faster region-based convolutional neural network (R-CNN), were also presented, in which the CNN was used to extract features and then region proposals were recorded from the input image in the form of anchors and bounding boxes [40; 41]. However, an overfitting issue was discovered when CNN models were employed with inadequate parameters. To address the overfitting issue, a semantic segmentation algorithm was developed with expand-contract dilation, which made the effective receptive field larger and reduced a large number of parameters [3; 42]. The CNN-based architectures discussed above failed to combat the issue of pose variation in CWD. A symmetry-driven Siamese network was presented for CWD to resolve the issue of pose variation [43]. Although the CNN-based architectures discussed above and different versions of the R-CNN architectures improved the accuracy of concealed object detection, they were ineffective for the detection of forbidden small objects [44; 45; 46; 47]. For the detection of small concealed objects, a multisource aggravation transformer (MATR) model was invented. The MATR model was composed of self-attention and cross-attention mechanisms to extract spatial and contextual details across the images with integration of the selective context module [48]. Though the transformer model paved a new way to raise the COD performance, the distance between the instrument receiver and target led to noise in the image reconstruction. To reduce the effect of noise due to the distance between the target and the receiver, the raw data from frequency-modulated continuous wave (FMCW) radars were captured and trained using ML and DL techniques, which surprisingly enhanced the accuracy of COD [49]. However, the setup of FMCW radars was complicated and costly. Hence, a low-cost MMW radar system of 77 GHz was implemented, using multi-scalefiltering and geometric (MSFG) augmentation to detect concealed weapons [50; 51; 52]. But the interference of environmental noise in the background of the images and the elevated time consumption of the MSFG algorithm restricted the use of the proposed algorithm in a real-life scenario. To counter the complex background issues and environmental noise and to lessen the time complexity of the algorithm, a combination of transformers and convolutional networks was deployed [53]. A task-aligned detection transformer was also introduced to increase the classification and detection accuracy [54]. However, transformer networks require high computational resources on large training data for faultless CWD systems. Therefore, other approaches, such as weakly supervised 2D pose adaptation-based segmentation, weight label correction, and spatio-temporal weighted methods, were developed. The above methods achieved SOTA performance [55; 56; 57].