# Single-Shot Object Detection with Split and Combine Blocks
## 1 Introduction  
Detecting objects from given images or videos is a fundamental but crucial research area in computer vision. Due to the excellent performance of feature extraction and representation learning, convolutional neural networks (CNNs) have become an important driver in promoting object detection progress [1]. With the emergence of large open source datasets and the availability of computation resources, CNNs-based object detectors flourish and can generally fall into two categories: one-stage approaches and two-stage approaches. Attributed to the higher computational efficiency and lower inference latency, one-stage detectors have gradually received more attention in real-time applications [2]. Scaling up CNNs by their depth, width or input image resolution is the most common and effective way to obtain better accuracy [3]. However, the ever-increasing trainable parameters, nonlinear activation units, and receptive fields cause difficulty with training and optimization. In order to overcome these obstacles, more effort is directed into devising connect patterns between different layers and modules to make the network efficient. In practice, many of the latest network structures emphasize the value of short paths from shallower layers to deeper layers. ResNet [4] proves the significance of skip connections for training deep networks in classification and localization tasks,and facilitates a dramatic variety of deep architectures. Dense convolutional network (DenseNet) [5] maximizes the feature transmission between different layers to improve parameter efficiency and reduce training difficulty. The input feature information of each layer in DenseNet is composed of the output of all preceding layers. Deep layer aggregation (DLA) [6] aggregates the feature hierarchy in a deeper method. DLA fuses feature information across various depths, scales, and resolutions to promote network performance, parameter efficiency, and reduce memory usage. Hourglass [7] processes input signals through repeated bottom-up, top-down inference paths, and then captures and merges information across all scales in this process. The excellent performance of these models indicates that feature fusion plays a central role in network performance. It is common practice to employ a feature pyramid to assist algorithms in capturing objects of a wide range of scales. Assign different priorities based on scales of object instances. High-level, low-resolution feature maps are generally responsible for large targets, while low-level, high-resolution feature maps are generally responsible for small targets. Due to the expensive computing and storage costs, recent studies desert the image pyramid, and instead exploit the inherent pyramid hierarchy formed in forward propagation to construct feature pyramids. The feature pyramid network (FPN) [8] introduces the lateral connections to integrate bottom-up pathways with top-down pathways. This architecture combines shallower, spatially strong features with deeper, semantically strong features, and ultimately enriches semantics for each feature maps. Adaptively spatial feature fusion (ASFF) [9] learns the weights of feature maps at all levels during propagation, and then aggregates feature maps of different scales based on these weights. AFF suppresses inconsistency between different feature maps with marginal computing cost. A multi-level feature pyramid network (MLFPN) [10] first fuses feature maps at all levels, then extracts and concatenates multi-depth feature maps of the equivalent scales, and finally utilizes Squeeze-and-Excitation networks (SENets) [11] to recalibrate the importance of each channel. These advanced feature pyramid variants deliver excellent performance gain, and do not demand much extra inference overhead, which demonstrates that good feature fusion strategies are the key to reach their full potential in visual tasks.