The primary objective of this study is to develop a novel object detection model that excels in both inference speed and accuracy, particularly under low-light conditions. Object detection plays a crucial role in various applications, from surveillance to autonomous vehicles, but remains challenging in low-light environments due to reduced visibility and image quality, along with high computational complexity. To tackle these issues, we propose a model that integrates cutting-edge deep learning techniques for efficient performance on resource-constrained devices. The model architecture comprises three key components: a backbone with Cross-Stage Partial (CSP) connections to reduce computational demands and enhance gradient flow; a neck for effective feature integration across scales; and a head for streamlined prediction of classifications and bounding box coordinates. The model also benefits from varifocal loss and complete IoU loss functions, which improve training convergence and accuracy. Additionally, advanced techniques such as task-assigned learning and mixed precision training enhance its performance across various benchmarks, making it well-suited for accurate object detection in low-light conditions.