As pedestrians are non-rigid objects， effective invariant representation of their visual features is the key to improving recognition performance. In natural visual scenes， moving pedestrians often undergo changes in scale， background， and pose， which creates obstacles for existing techniques for extracting these irregular features. The issue was addressed by exploring the problem of invariant recognition of moving pedestrians based on the neural structural characteristics of mammalian retinas， and a Moving Pedestrian Detection Neural Network （MPDNN） was proposed for visual scenes. MPDNN was composed of two neural modules： the presynaptic network and the postsynaptic network. The presynaptic network was used to perceive low-level visual motion cues representing the moving object and extract the object’s binarized visual information， and the postsynaptic network was utilized to take advantage of the sparse invariant response properties in the biological visual system and use the invariant relationship between large concave and convex regions of the object’s contour after continuous shape changes， then， stably changed visual features were encoded from low-level motion cues to build invariant representations of pedestrians. Experimental results show that MPDNN achieves a 96.96% cross-domain detection accuracy on the public datasets CUHK Avenue and EPFL， which is 4.52 percentage points higher than the SOTA （State of the Art） model； MPDNN demonstrates good robustness on scale and motion posture variation datasets， with accuracy of 89.48% and 91.45%， respectively. The effectiveness of the biological invariant object recognition mechanism in moving pedestrian detection was validated by the above experimental results.

Automatic identification of marine ships plays an important role in alleviating the pressure of marine traffic. To address the problem of low automatic ship identification rate， a ship identification model based on ResNet50 （Residual Network50） and improved attention mechanism was proposed. Firstly， a ship data set was made by ourselves， and divided into the training set， the verification set and the test set， which were augmented by blurring and adding noise. Secondly， an improved attention module — Efficient Spatial Pyramid Attention Module （ESPAM） and ship type recognition model ResNet50_ESPAM were designed. Finally， the ResNet50_ESPAM was trained， verified and compared with other commonly used neural network models using ship data sets. The experimental results show that in the verification set， the highest accuracy of ResNet50_ESPAM is 95.5%， and the initial accuracy is 81.2%； compared with AlexNet（Alex Krizhevsky Network）， GoogleNet （Google Inception Net）， ResNet34（Residual Network34）， ResNet50 and ResNet50_CBAM （ResNet50_Convlutional Block Attention Module）， the maximum accuracy of the model validation set increases by 5.1， 4.9， 2.6， 1.6 and 1.4 percentage points respectively， and the initial accuracy of the validation set increases by 49.4， 44.7， 27.7， 3.0 and 2.1 percentage points respectively， indicating that ResNet50_ESPAM has a high recognition accuracy in ship type recognition， and the improved attention module ESPAM is highly effective.

In order to obtain the SAR images which include the typical target of interest, a new method of SAR images screening based on bit-plane characteristics was proposed according to the imaging characteristic of target. Based on the suitable gray pretreatment to the images, the target’s prior knowledge was analyzed, the significant bit-plane image was paid attention by the measurement of bit-plane complexity, run length and frequency spectrum. And then SAR images were screened combined with the gray histogram features. Around the airport SAR images, experiment shows that the method can screen the images rapidly. Besides, the airport target is extracted successfully, which can satisfy the requirements.