Weapon Identification Research Articles

Magazine Identification on SS1-V1 Assault Rifle using Web-based HX711 Load Cell Sensor

The SS1-V1 is an assault rifle model equipped with a magazine as one of its main components. The magazine plays a crucial role in storing and loading ammunition. However, magazines must be stored separately from the weapon as their integrated storage can pose a risk to a country’s security. Therefore, this research proposes a web-based system capable of identifying the presence of magazines in weapons in real-time. This system is supported by various hardware components, including a load cell sensor, HX711 sensor module, Arduino UNO R3, and an Ethernet shield for network connectivity. In addition, API is used for data management, which is then stored in the database. The results of this research indicate that the average response time for each rack within a cabinet is between 2.7s to 3.3s, while for racks serving as slaves, it ranges from 14.16s to 15.01s. Based on the results of the weight-based weapon identification testing, there is a weight difference of 0.1kg to 0.2kg. These results state that all tests were successfully identified by the web system according to the conditions of the weapons on the rack.

Harnessing Deep Learning for Video Based Weapon Detection

This research addresses the escalating global issue of handgun-related crimes by proposing an innovative Intelligent Video Surveillance System (IVSS) that leverages advanced deep learning (DL) techniques for remote firearm detection and timely threat response. The system employs Convolutional Neural Networks (CNN) and the YOLO v3 model, uniquely integrating Transfer Learning (TL) to enhance adaptability and efficacy. Experimental validation using the Internet Movie Firearms Database (IMFDB) demonstrates the system's versatility in detecting various pistols and guns, achieving promising results that surpass existing systems in accuracy and efficiency. Challenges in real-time weapon recognition, such as the absence of a standardized weapon dataset, occlusion, and small object sizes, are acknowledged. Emphasis is placed on the critical need for reliable data acquisition, precise labeling, and preprocessing tailored to different detection algorithms. The implementation encompasses video collection, preprocessing, model loading, algorithm application, segmentation, and classification, alongside a user-friendly webcam interface for real-time detection. Additionally, the system integrates the pyttsx3 library for voice alerts and the Twilio API for voice call alerts to enhance responsiveness. In summary, this study presents a novel CNN-based model combining Transfer Learning with YOLO v3, achieving superior weapon identification and distinguishing between real and fake firearms, representing a significant advancement in intelligent video surveillance and contributing to the reduction of weapon violence.

Emotion expression salience and racially biased weapon identification: A diffusion modeling approach.

Racial stereotypes are commonly activated by informational cues that are detectable in people's faces. Here, we used a sequential priming task to examine whether and how the salience of emotion (angry/scowling vs. happy/smiling expressions) or apparent race (Black vs. White) information in male face primes shapes racially biased weapon identification (gun vs. tool) decisions. In two experiments (Ntotal = 546) using two different manipulations of facial information salience, racial bias in weapon identification was weaker when the salience of emotion expression versus race was heightened. Using diffusion decision modeling, we tested competing accounts of the cognitive mechanism by which the salience of facial information moderates this behavioral effect. Consistent support emerged for an initial bias account, whereby the decision process began closer to the "gun" response upon seeing faces of Black versus White men, and this racially biased shift in the starting position was weaker when emotion versus race information was salient. We discuss these results vis-à-vis prior empirical and theoretical work on how facial information salience moderates racial bias in decision-making.

Cognitive Processes Underlying the Weapon Identification Task: A Comparison of Models Accounting for Both Response Frequencies and Response Times

The weapon identification task (WIT) is a sequential priming paradigm designed to assess effects of racial priming on visual discrimination between weapons (guns) and innocuous objects (tools). We compare four process models that differ in their assumptions on the nature and interplay of cognitive processes underlying prime-related weapon-bias effects in the WIT. All four models are variants of the process dissociation procedure, a widely used measurement model to disentangle effects of controlled and automatic processes. We formalized these models as response time-extended multinomial processing tree models and applied them to eight data sets. Overall, the default interventionist model (DIM) and the preemptive conflict-resolution model (PCRM) provided good model fit. Both assume fast automatic and slow controlled process routes. Additional comparisons favored the former model. In line with the DIM, we thus conclude that automatically evoked stereotype associations interfere with correct object identification from the outset of each WIT trial.

A probabilistic model for murder weapon identification using stab-marks in human ribs.

The aim of this article is to provide a scientific and statistical basis to identify the murder weapon in stabbing cases from the geometric characteristics of the stab-marks left on human ribs. For this purpose, a quantitative predictive model is developed, based on geometric measurements of the stab-mark and its location along the rib. A general method based on Bayesian inference and probabilities is used for the model development, rather than a deterministic model given its inability in certain occasions to identify the murder weapon. Following the process explained in this article to collect the stab-mark information required, the complete probabilistic model exposed attained a high accuracy in the identification of the murder weapon between two macroscopically identical blades with a microscopic alteration in one of them (more than 90% of correct identification is achieved).

Automatic Prejudice and Weapon Identification: A Study with Students and Police Officers

Automatic Prejudice and Weapon Identification: A Study with Students and Police Officers

Guns Are Not Faster to Enter Awareness After Seeing a Black Face: Absence of Race-Priming in a Gun/Tool Task During Continuous Flash Suppression.

In the Weapon Identification Task (WIT), Black faces prime the identification of guns compared with tools. We measured race-induced changes in visual awareness of guns and tools using continuous flash suppression (CFS). Eighty-four participants, primed with Black or Asian faces, indicated the location of a gun or tool target that was temporarily rendered invisible through CFS, which provides a sensitive measure of effects on early visual processing. The same participants also completed a standard (non-CFS) WIT. We replicated the standard race-priming effect in the WIT. In the CFS task, Black and Asian primes did not affect the time guns and tools needed to enter awareness. Thus, race priming does not alter early visual processing but does change the identification of guns and tools. This confirms that race-priming originates from later post-perceptual memory- or response-related processing.

An Expert System for Weapon Identification and Categorization Using Machine Learning Technique to Retrieve Appropriate Response

In response to any terrorist attack on hospitals, airports, shopping malls, schools, universities, colleges, railway stations, passport offices, bus stands, dry ports and the other important private and public places, a proper plan will need to be developed effective response. In normal moments, security guards are deployed to prevent criminals from doing anything wrong. For example, someone is moving around with a weapon, and security guards are watching its movement through closed circuit television (CCTV). Meanwhile, they are trying to identify his weapon in order to plan an appropriate response to the weapon he has. The process of manually identifying weapons is man-made and slow, while the security situation is critical and needs to be accelerated. Therefore, an automated system is needed to detect and classify the weapon so that appropriate response can be planned quickly to ensure minimal damage. Subject to previous concerns, this study is based on the Convoluted Neural Network (CNN) model using datasets that are assembled on the YOLO and you only see once. Focusing on real-time weapons identification, we created a data collection of images of multiple local weapons from surveillance camera systems and YouTube videos. The solution uses parameters that describe the rules for data generation and problem interpretation. Then, using deep convolutional neural network models, an accuracy of 97.01% is achieved.

Anger, race, and the neurocognition of threat: attention, inhibition, and error processing during a weapon identification task

This study measured event-related brain potentials (ERPs) to test competing hypotheses regarding the effects of anger and race on early visual processing (N1, P2, and N2) and error recognition (ERN and Pe) during a sequentially primed weapon identification task. The first hypothesis was that anger would impair weapon identification in a biased manner by increasing attention and vigilance to, and decreasing recognition and inhibition of weapon identification errors following, task-irrelevant Black (compared to White) faces. Our competing hypothesis was that anger would facilitate weapon identification by directing attention toward task-relevant stimuli (i.e., objects) and away from task-irrelevant stimuli (i.e., race), and increasing recognition and inhibition of biased errors. Results partially supported the second hypothesis, in that anger increased early attention to faces but minimized attentional processing of race, and did not affect error recognition. Specifically, angry (vs. neutral) participants showed increased N1 to both Black and White faces, ablated P2 race effects, and topographically restricted N2 race effects. Additionally, ERN amplitude was unaffected by emotion, race, or object type. However, Pe amplitude was affected by object type (but not emotion or race), such that Pe amplitude was larger after the misidentification of harmless objects as weapons. Finally, anger slowed overall task performance, especially the correct identification of harmless objects, but did not impact task accuracy. Task performance speed and accuracy were unaffected by the race of the face prime. Implications are discussed.

Artificial Intelligence and Deep Learning for Weapon Identification in Security Systems

As crime rates rise at large events and possibly lonely places, security is always a top concern in every field. A wide range of issues may be solved with the use of computer vision, including anomalous detection and monitoring. Intelligence monitoring is becoming more dependent on video surveillance systems that can recognise and analyse scene and anomaly occurrences. Using SSD and Faster RCNN techniques, this paper provides automated gun (or weapon) identification. Use of two different kinds of datasets is included in the proposed approach. As opposed to the first dataset, the second one comprises pictures that have been manually tagged. However, the trade-off between speed and precision in real-world situations determines whether or not each method will be useful.

Category salience and racial bias in weapon identification: A diffusion modeling approach.

Stereotypes linking Black Americans with guns can have life-altering outcomes, making it important to identify factors that shape such weapon identification biases and how they do so. We report 6 experiments that provide a mechanistic account of how category salience affects weapon identification bias elicited by male faces varying in race (Black, White) and age (men, boys). Behavioral analyses of error rates and response latencies revealed that, when race was salient, faces of Black versus White males (regardless of age) facilitated the classification of objects as guns versus tools. When a category other than race was salient, racial bias in behavior was reduced, though not eliminated. In Experiments 1-4, racial bias was weaker when participants attended to a social category besides race (i.e., age). In Experiments 5 and 6, racial bias was weaker when participants attended to an applicable, yet nonsubstantive category (i.e., the color of a dot on the face). Across experiments, process analyses using diffusion models revealed that, when race was salient, seeing Black versus White male faces led to an initial bias to favor the "gun" response. When a category besides race (i.e., age, dot color) was salient, racial bias in the relative start point was reduced, though not eliminated. These results suggest that the magnitude of racial bias in weapon identification may differ depending on what social category is salient. The collective findings also highlight the utility of diffusion modeling for elucidating how category salience shapes processes underlying racial biases in behavior. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Identifikacija vojnika i naoružanja u magacinu naoružanja zasnovana na poređenju procesiranja slika i RFID tagova

Introduction/purpose: The process of issuing and retrieving weapons in the military should be fast enough and should provide immediate availability of accurate information on the status of weapons. Methods: This paper deals with the problem of digitizing the recording of issuing and returning weapons through the use of modern Edge computing technology. The problem is presented through two approaches. The first approach is based on the application of machine learning algorithms for recognizing the serial number of a weapon based on the camera image, while the second approach concerns the application of RFID technology. User authentication is based on the application of biometrics. Results: The results obtained from testing the architecture for identifying weapons using a camera indicate that such an architecture is not suitable for identifying weapons. A weapon identification solution using RFID technology overcomes the problems of the previously mentioned solution. However, RFID technology requires additional modifications regarding the implementation of tags on or into weapons so that readings can be made. Conclusion: The implemented weapon identification solution based on RFID technology and a user identification solution with biometric authentication enables easy and reliable identification, speed of issuing and retrieval of weapons, network relieving, and real-time monitoring of the weapon status.

Improving the Accuracy of Firearm Identification in a Dynamic Use of Force Scenario

Law enforcement officers are sometimes required to make split-second use of force decisions. One factor that can impact their decision-making process is the presence of a weapon. This experiment sought to improve the speed and accuracy of weapon identification in a dynamic use of force scenario through the principles of deliberate practice. This research utilized randomized control trial with random assignment to either a control or test condition. Eighty-seven participants completed the pretest, intervention, and posttest. Participants’ vision was recorded via a mobile vision-tracker. With only 20 minutes of training, the test group made 1/3 the amount of decision errors as the control group (Cohen’s d = 0.95). The test group was about 16% faster than the control group at visually finding the object in the suspect’s hand and determining if it was a gun or not (Cohen’s d = 0.91).

Research on Target Segmentation Method Based on Maximum Entropy Method

According to the background requirements of the target guided weapon identification, the different characteristics of the existing threshold segmentation methods are analyzed, a threshold calculation method based on maximum entropy is studied, and the aerial targets and ground target images collected by a certain type of equipment are used to adopt two-dimensional. The maximum entropy method is used to perform threshold segmentation experiments. By comparing the segmentation effects of different methods on aerial targets and ground target images, it is concluded that the maximum entropy method has better resistance to background interference and universal applicability.

Mindfulness Training, Implicit Bias, and Force Response Decision-making.

The goal of this study was to assess the preliminary efficacy of a Mindfulness-Based Training (Mindfulness-based Resilience Training; MBRT) in improving weapon identification among Law Enforcement Officers (LEOs). Participants (N = 61) were randomly assigned to either MBRT or a no-intervention control group (NIC). A self-report questionnaire assessing mindfulness and a computerized measure assessing implicit stereotype reliance were administered at baseline, post-training, and three months following intervention completion. We also collected information about meditation practice outside of the training for LEOs in the MBRT group. Inferential analyses yielded improvements in mindfulness in the MBRT group compared to NIC. Analyses did not provide evidence for implicit stereotype reliance at baseline and therefore did not yield a significant impact of MBRT versus NIC on implicit stereotype reliance, ps > .05; however, participants across both conditions exerted more control when responding to Black male targets compared to White male targets, F(1,74) = 3.98, p = .05, 95% CI [-.05, -.01], d = .36. Our results do not provide evidence for the impact of MBRT on weapon identification, but do suggest that LEOs exerted more effort when responding to images of Black males compared to White males. We discuss recommendations for future clinical trials assessing implicit stereotype reliance, viz., that researchers utilize measures more sensitive to a wider range of LEO samples and with higher ecological validity, and we discuss potential reasons why our results do not align with past research.

Air-Attack Weapon Identification Model of Weighted Navie Bayes Based on SOA

Traditional Navie Bayes algorithm exists the issues of low inefficiency for the Air-attack Weapon Identification. Inorder to solve this problem, Air-attack Weapon Identification model of Weighted Navie Bayes Based on Seeker Optimization Algorithm is proposed. Firstly, the model reduces the dimension of the data sam-ples using rough set theory. Secondly, Seeker Optimization Algorithm searches the best attribute weights of Weighted Naïve Bayes. Finally, Navie Bayes classifier is structured with the best attribute weights to complete detection. The combination of the two algorithms can not only solve the feature redundancy problem of the traditional Navie Bayes algorithm, but also can optimize the strong independence between features. Through the experiments, prove that using this model for Air-attack Weapon Identification in air defense combat identification

The Shape of ROC Curves in Shooter Tasks: Implications for Best Practices in Analysis

Four experiments addressed the widely studied issue of the association between racial groups and guns, namely shooter bias, as measured in the first-person shooter task or the weapon identification task, in which participants judge whether a suspect has a weapon or some other item such as a phone (Correll, Park, Judd, & Wittenbrink, 2002; Payne, 2001). Previous studies have employed various analyses that make conflicting, and indeed untested, assumptions about the underlying nature of the data: Analyses of variance and model-based analyses assume linear receiver operating characteristics (ROCs) and signal detection (SDT) analyses assume curved ROCs. The present experiments directly investigated the shape of the ROCs for the weapon identification task, demonstrating that they are curved, and that the majority of previous studies are at risk for inclusion of inappropriate analyses, because they assume linear rather than curved ROCs.

Eyewitness Memory for Firearms: Narrative Accounts and Specific Questioning in the Elucidation of Accurate Information

Much eyewitness research has addressed memory for persons and scenes, but limited work has addressed memory for firearms, frequently an important point in investigations and in court. The present research addressed this topic using the format of a traditional police interview, in which seventy subjects provided unrestricted free recall of a weapon seen in a crime situation, followed by responses to specific questions. Three firearms were used in a between-subjects format: a typical modern semi-automatic pistol, a less-typical Old West revolver, and an atypical single-shot muzzle-loading pistol. In the free recall stage, respondents provided about four times as many correct as incorrect details. However, in the specific-question stage, there were only 1.2 times as many new correct responses as new incorrect details, consistent with current cognitive theory. No difference between the weapons was observed in the production of correct responses, but the revolver and single-shot pistol resulted in more incorrect responses than did the modern semi-automatic, regardless of the saliency of weapon features. These results demonstrate the importance of the original, initial free recall phase in developing accurate identification of a given weapon, and of the type of weapon involved, and have important implications for police interviewing for accurate weapon identification.

Making sense of the noise: Replication difficulties of Correll's (2008) modulation of 1/f noise in a racial bias task.

Correll (Journal of Personality and Social Psychology, 94, 48-59, 2008; Study 2) found that instructions to use or avoid race information decreased the emission of 1/f noise in a weapon identification task (WIT). These results suggested that 1/f noise in racial bias tasks reflected an effortful deliberative process, providing new insights regarding the mechanisms underlying implicit racial biases. Given the potential theoretical and applied importance of understanding the psychological processes underlying implicit racial biases - and in light of the growing demand for independent direct replications of findings to ensure the cumulative nature of our science - we attempted to replicate Correll's finding in two high-powered studies. Despite considerable effort to closely duplicate all procedural and methodological details of the original study (i.e., same cover story, experimental manipulation, implicit measure task, original stimuli, task instructions, sampling frame, population, and statistical analyses), both replication attempts were unsuccessful in replicating the original finding challenging the theoretical account that 1/f noise in racial bias tasks reflects a deliberative process. However, the emission of 1/f noise did consistently emerge across samples in each of our conditions. Hence, future research is needed to clarify the psychological significance of 1/f noise in racial bias tasks.

Making Sense of the Noise: Two Unsuccessful High-Powered Replication Attempts of Correll's (2008) 1/f Noise Racial Bias Effect

Correll (2008, Study 2) found that instructions to use or avoid race information decreased the emission of 1/f noise in a weapon identification task (WIT). These results suggested that 1/f noise in racial bias tasks reflected an effortful deliberative process, providing new insights regarding the mechanisms underlying implicit racial biases. Given the potential theoretical and applied importance of understanding the psychological processes underlying implicit racial biases – and in light of the growing demand for independent direct replications of findings to ensure the cumulative nature of our science – we attempted to replicate Correll’s finding in two high-powered studies. Despite considerable effort to closely duplicate all procedural and methodological details of the original study (i.e., same cover story, experimental manipulation, implicit measure task, original stimuli, task instructions, sampling frame, population, and statistical analyses), both replication attempts were unsuccessful in replicating the original finding challenging the theoretical account that 1/f noise in racial bias tasks reflects a deliberative process. However, the emission of 1/f noise did consistently emerge across samples in each of our conditions. Hence, future research is needed to clarify the psychological significance of 1/f noise in racial bias tasks.