  Object Detection has been a significant topic in computer vision. As the
continuous development of Deep Learning, many advanced academic and industrial
outcomes are established on localising and classifying the target objects, such
as instance segmentation, video tracking and robotic vision. As the core
concept of Deep Learning, Deep Neural Networks (DNNs) and associated training
are highly integrated with task-driven modelling, having great effects on
accurate detection. The main focus of improving detection performance is
proposing DNNs with extra layers and novel topological connections to extract
the desired features from input data. However, training these models can be
computationally expensive and laborious progress as the complicated model
architecture and enormous parameters. Besides, the dataset is another reason
causing this issue and low detection accuracy, because of insufficient data
samples or difficult instances. To address these training difficulties, this
thesis presents two different approaches to improve the detection performance
in the relatively light-weight way. As the intrinsic feature of data-driven in
deep learning, the first approach is "slot-based image augmentation" to enrich
the dataset with extra foreground and background combinations. Instead of the
commonly used image flipping method, the proposed system achieved similar mAP
improvement with less extra images which decrease training time. This proposed
augmentation system has extra flexibility adapting to various scenarios and the
performance-driven analysis provides an alternative aspect of conducting image
augmentation
