Object detection techniques are used to determine object categories and localization in images. Different tasks have different requirements for detection efficiency and accuracy, which leads to high efficiency one-stage object detection methods and high-precision two-stage object detection methods. As the main category of difficult detection objects, small objects have the characteristics of few pixels, wide distribution, and complex background. Many serious tasks in special environments, such as industrial defect detection, large-scale pedestrian statistics, remote sensing image recognition, etc. require the model to have high accuracy for the detection of difficult objects. Aiming at the problem of information loss of small objects in deep network features, this paper proposes a feature levels selection method for backbone network based on effective receptive fields. By moving down the feature level of the backbone network, the size of the effective receptive field corresponding to the selected hierarchical features is matched with the scale distribution of the small objects to be detected to obtain the best small object detection performance. According to the effective receptive field size (ERF), the appropriate Shallow Feature Selection (SFS) is selected to participate in the subsequent detection process to ensure that accurate and sufficient small target information is extracted from the backbone network.Based on one-stage and two-stage object detection methods, this paper proposes improvement measures for small object detection from the data level, network level and training level, and designs experiments and validates them.

Traffic flow counting is an object detection problem. YOLO (" You Only Look Once ") is a popular object detection network. Spiking-YOLO converts the YOLO network from ANN(artificial neural network) to SNN (spiking neural network), which consumes less power than YOLO. However, Spiking-YOLO is difficult to train and has poor accuracy because of overfitting. Dropout and DropConnect are two methods to regularize neural network models and have produced cutting-edge outcomes in many benchmarks. The two drop approaches are first applied to Spiking-YOLO. Considering that a higher membrane potential of a biological neuron implies a higher probability of neural activation, we propose three adaptive drop algorithms—Spiking-YOLO with adaptive Dropout, Spiking-YOLO with adaptive DropConnect, and Spiking-YOLO with group adaptive Drop, which adaptively adjusts the keep probability for training SNNs. The experimental results show that the adaptive algorithms have fast convergence speed with high detection accuracy and mAP value, and work well in the case of high-speed streaming video, making them suitable for online learning scenarios.