###### Abstract  
Small target detection has always been a hot and difficult point in the field of target detection. The existing detection network has a good effect on conventional targets but a poor effect on small target detection. The main challenge is that small targets have few pixels and are widely distributed in the image, so it is difficult to extract effective features, especially in the deeper neural network. A novel plug-in to extract location features of the small target in the deep network was proposed. Because the deep network has a larger receptive field and richer global information, it is easier to establish global spatial context mapping. The plug-in named _location feature extraction_ establishes the spatial context mapping in the deep network to obtain the global information of scattered small targets in the deep feature map. Additionally, the attention mechanism can be used to strengthen attention to the spatial information. The comprehensive effect of the above two can be utilized to realize location feature extraction in the deep network. In order to improve the generalization of the network, a new self-distillation algorithm was designed for pre-training that could work under self-supervision. The experiment was conducted on the public datasets (Pascal VOC and Printed Circuit Board Defect dataset) and the self-made dedicated small target detection dataset, respectively. According to the diagnosis of the false-positive error distribution, the location error was significantly reduced, which proved the effectiveness of the plug-in proposed for location feature extraction. The \(mAP\) results can prove that the detection effect of the network applying the location feature extraction strategy is much better than the original network. location feature extraction; small target detection; self-knowledge distillation; attention mechanism +
Footnote †: journal:  
The main method to solve these problems is **multi-scale feature fusion**. The deep network has rich semantic information, but it lacks the fine-grained information of the shallow network. The semantic information and fine-grained information can be used simultaneously by fusing features from multiple scales, providing more comprehensive feature information for small target detections. Feature Pyramid Net (FPN) [10] is applied in many small target detections. It adopts multi-scale feature fusion and uses the fused results to make predictions. In the Single Shot MultiBox Detector (SSD) [11] series, DSSD [12] deconvolutes the deep feature map based on SSD and multiplies it with the shallow feature to obtain a better multi-layer feature map, which is very beneficial to the detection of small objects. In the fast RCNN series, HyperNet [13] fused the feature maps obtained from the first, third, and fifth convolution groups, pooled the shallow features, deconvoluted the deep features, and finally fused them in the way of channel concatenation to complement each other. In general, multi-scale feature map fusion is helpful to capture detailed information and rich semantic information, which is convenient for object location and classification, respectively. However, the multi-scale representation method does not propose a specific feature. Widely extracting features from multiple layers will inevitably increase the computational burden. In addition, redundant information fusion may lead to background noise and insufficient performance [14]. Because small objects only occupy a small part of the image, the information obtained directly from local areas is very limited. Every object always exists in a specific environment or coexists with other objects. Then, some **context-based detection methods** are proposed to utilize the relationship between small objects and other objects or backgrounds. FASSD [15] uses additional features from different layers as the context and proposes an attention mechanism to focus on the context information from the target layer.