The construction industry faces high risks due to frequent accidents, often leaving workers in perilous situations where rapid response is critical. Traditional safety monitoring methods, including wearable sensors and GPS, often fail under obstructive or indoor conditions. This research introduces a novel real-time scream detection and localization system tailored for construction sites, especially in low-resource environments. Integrating Wav2Vec2 and Enhanced ConvNet models for accurate scream detection, coupled with the GCC-PHAT algorithm for robust time delay estimation under reverberant conditions, followed by a gradient descent-based approach to achieve precise position estimation in noisy environments. Our approach combines these concepts to achieve high detection accuracy and rapid localization, thereby minimizing false alarms and optimizing emergency response. Preliminary results demonstrate that the system not only accurately detects distress calls amidst construction noise but also reliably identifies the caller's location. This solution represents a substantial improvement in worker safety, with the potential for widespread application across high-risk occupational environments. The scripts used for training, evaluation of scream detection, position estimation, and integrated framework will be released at: https://github.com/Anmol2059/construction_safety.

翻译：建筑行业因事故频发而面临高风险，工人常处于危急状态，快速响应至关重要。传统的安全监测方法（包括可穿戴传感器和GPS）在遮挡或室内条件下往往失效。本研究提出一种专为建筑工地（特别是在低资源环境中）设计的实时尖叫检测与定位系统。该系统集成Wav2Vec2与增强型ConvNet模型以实现精准尖叫检测，结合GCC-PHAT算法在混响环境下实现鲁棒的时延估计，并采用基于梯度下降的方法在噪声环境中实现精确位置估计。我们的方法融合这些技术，实现了高检测精度与快速定位，从而最大限度减少误报并优化应急响应。初步结果表明，该系统不仅能准确识别建筑噪声中的求救呼叫，还能可靠确定呼叫者位置。该方案显著提升了工人安全水平，具备在高风险职业环境中广泛应用的潜力。用于训练、尖叫检测评估、位置估计及集成框架的代码将发布于：https://github.com/Anmol2059/construction_safety。