# Buffer-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection
## 1 Introduction  
An impeccable concealed threat detection system delivers contact-free security screening across various places, including airport checkpoints, warehouses, sports stadiums, religious congregations, and concurrent productions [1, 2, 3]. The detection of concealed weapons and improvised explosive devices employed by terrorists has been a demanding milestone, underscoring the need to strengthen global counter-terrorism efforts to ensure invulnerable public security systems [4]. The Global Terrorism Index report of 2024 indicates that terrorism led to 8352 deaths in 2023, identifying a 22 percent increase from the previous year, especially in two countries, i.e., Burkina Faso and Pakistan, as shown in Figure 1[5]. Hence, improvements in the efficiency of existing concealed object detection(COD) systems are required to address a diverse array of hidden threats, encompassing weapons, explosives, and contraband. Traditional security screening equipment, such as X-ray machines, infrared-based imaging systems, and metal detectors, have certain constraints. For example, infrared-based imaging systems fail to detect things hidden behind baggy garments [6; 7]. However, X-rays have negative effects on the health of people in real-world scenarios [8; 9; 10]. Furthermore, metal detectors are vulnerable to insensitivity issues caused by non-metallic or tiny objects [11]. The above shortcomings have been eliminated to a large extent by using a terahertz (THz)-wave imaging system [12]. THz technologies can operate in two modes: passive or active. PMMW imaging technologies have gained a positive reputation since they exert no risk to human health [13; 14; 15]. PMMW sensors penetrate through non-metallic materials convincingly when compared to the aforementioned screening methods [16; 17; 18]. Despite the benefits of PMMW systems, PMMW systems experience enormous complications. The performance of PMMW systems deteriorates because the image datasets acquired from PMMW imaging screening systems struggle with low SNR and contrast, resulting in picture fuzziness [12]. Furthermore, PMMW imaging systems suffer from environmental noise and illumination problems due to complex light environments, unconstrained conditions, and view-point variations [19]. The above-stated drawbacks make detection one of the most fundamental and intimidating tasks in computer vision (CV) and artificial intelligence (AI). Recent advances in CV, AI, and machine learning (ML) approaches have led to novel methods for quick, contact-free, and robust identification of concealed objects in PMMW imaging systems [20; 21]. Unfortunately, by addressing one of the above-mentioned issues independently in a single study, the state-of-the-art (SOTA) ML, AI, and CV algorithms boost the performance of the COD system. However, the CV concerns mentioned above may concurrently affect the data obtained from the PMMW imaging equipment. Therefore, the SOTA ML, AI, and CV algorithms can show elevated false positive rates. As a result, this study proposes a novel three-stage cascade idea to develop a new algorithm that combats every CV challenge simultaneously. Furthermore, a bibliometric analysis of research articles published after 1990 demonstrates the research gap in the existing literature. The extensive literature survey reveals that few studies on weapon identification have been performed using traditional approaches, as seen in the blue cluster in Figure 2. However, the profound knowledge of the dull brown and dull yellow clusters of the bibliometric analysis emphasizes the severe necessity for rigorous study in the field of concealed weapon detection (CWD) employing PMMW imaging systems, since the words \"cwd\" and \"passive millimeter wave imaging\" are very  
Figure 1: Total terrorism deaths by country in 2022–2023. dim as compared to other keywords in the dull brown and dull yellow clusters, respectively.