Since safety plays a crucial role and the top priority, in both unmanned and driver-assistance driving systems, there is a need of efficient and accurate detection of captured objects by object detection algorithms in real-time. Directly applying existing models to tackle real-time pedestrian and vehicle detection tasks captured by high speed moving vehicle scenarios has two problems. First, the target scale varies drastically because the vehicle speed changes greatly. Second, captured images contain both tiny targets and high density targets, which brings in occlusion between targets. To solve the two issues, an efficient light weight real-time detection algorithm is proposed, which is referred to as EfficientLiteDet. Based on Tiny-YOLOv4, one more prediction head is introduced in the proposed model to detect multi-scale targets effectively. In order to detect tiny and occluded denser targets, we used Transformer Prediction Heads (TPH) instead of original anchor detection heads in our model. To explore the potential of self-attention mechanism in TPH, the proposed model integrates “convolutional block attention model” to locate crucial attention region on scenarios with denser targets. Further to improve the detection performance of our model, we applied various data augmentation strategies such as mosaic, mix-up, multi-scale, and random-horizontal-flip during the model training. Extensive experiments are conducted on five challenging pedestrian and vehicle datasets shows that the EfficientLiteDet model has better performance in real-time scenarios. On Pascal Voc-2007, Highway and Udacity datasets, the proposed model achieves mean average precision (mAP) 87.3%, 80.1% and 77.8%, respectively, which is quite better than Tiny-YOLOv4 state-of-the-art algorithm by + 2.4%, 1.8% and + 2.4%, respectively.

由于安全起着至关重要的作用和重中之重，在无人驾驶和辅助驾驶系统中，都需要通过对象检测算法实时高效、准确地检测捕获的对象。直接应用现有模型来处理高速移动车辆场景捕获的实时行人和车辆检测任务有两个问题。首先，由于车速变化很大，目标规模变化很大。其次，捕获的图像包含微小目标和高密度目标，这会导致目标之间的遮挡。针对这两个问题，提出了一种高效的轻量级实时检测算法，称为EfficientLiteDet。在Tiny-YOLOv4的基础上，在所提出的模型中引入了一个额外的预测头，以有效地检测多尺度目标。为了检测微小且被遮挡的密集目标，我们在模型中使用了 Transformer Prediction Heads (TPH) 而不是原始的锚检测头。为了探索 TPH 中自我注意机制的潜力，所提出的模型集成了“卷积块注意模型”，以在目标更密集的场景中定位关键注意区域。为了进一步提高我们模型的检测性能，我们在模型训练期间应用了各种数据增强策略，例如马赛克、混合、多尺度和随机水平翻转。在五个具有挑战性的行人和车辆数据集上进行了广泛的实验，表明 EfficientLiteDet 模型在实时场景中具有更好的性能。在 Pascal Voc-2007、Highway 和 Udacity 数据集上，所提出的模型实现了平均精度 (mAP) 87.3%、80.1% 和 77.8%，