Power line detection plays an important role in an automated UAV-based electricity inspection system, which is crucial for real-time motion planning and navigation along power lines. Previous methods which adopt traditional filters and gradients may fail to capture complete power lines due to noisy backgrounds. To overcome this, we develop an accurate power line detection method using convolutional and structured features. Specifically, we first build a convolutional neural network to obtain hierarchical responses from each layer. Simultaneously, the rich feature maps are integrated to produce a fusion output, then we extract the structured information including length, width, orientation and area from the coarsest feature map. Finally, we combine the fusion output with structured information to get a result with clear background. The proposed method fully exploits multiscale and structured prior information to conduct both accurate and efficient detection. In addition, we release two power line datasets due to the scarcity in the public domain. The method is evaluated on the well-annotated power line datasets and achieves competitive performance compared with state-of-the-art methods.

Robust object detection is hindered by various illumination conditions in real-world applications. Common practice introduces thermal modality to augment the detection capability of RGB images in poor illumination conditions. However, a major challenge for such work is how to leverage the complementary information of RGB and thermal images effectively. In this article, we tackle this by combining the strengths of illumination-guided inter- and intra-modality information in both modalities. Specifically, we propose an effective object detection network dubbed Triple-I Net (TINet) to adaptively fuse the complementary features extracted from RGB and thermal images. First, we design an illumination-guided feature weighting module to guide the network in learning toward a reliable modality. Then, an inter-modality attention module is developed to amplify and complement differential features between these two modalities. We further design an intra-modality attention module using object heatmap prediction to enhance the foreground features of each modality. Finally, we fuse the inter- and intra-modality features for object detection with illumination-guided feature weights. Experimental results demonstrate the effectiveness of the proposed method on the FLIR-aligned dataset and the KAIST multispectral pedestrian detection dataset.

Scene classification plays an important role in understanding high-resolution satellite (HRS) remotely sensed imagery. For remotely sensed scenes, both color information and texture information provide the discriminative ability in classification tasks. In recent years, substantial performance gains in HRS image classification have been reported in the literature. One branch of research combines multiple complementary features based on various aspects such as texture, color and structure. Two methods are commonly used to combine these features: early fusion and late fusion. In this paper, we propose combining the two methods under a tree of regions and present a new descriptor to encode color, texture and structure features using a hierarchical structure-Color Binary Partition Tree (CBPT), which we call the CTS descriptor. Specifically, we first build the hierarchical representation of HRS imagery using the CBPT. Then we quantize the texture and color features of dense regions. Next, we analyze and extract the co-occurrence patterns of regions based on the hierarchical structure. Finally, we encode local descriptors to obtain the final CTS descriptor and test its discriminative capability using object categorization and scene classification with HRS images. The proposed descriptor contains the spectral, textural and structural information of the HRS imagery and is also robust to changes in illuminant color, scale, orientation and contrast. The experimental results demonstrate that the proposed CTS descriptor achieves competitive classification results compared with state-of-the-art algorithms.

The event camera asynchronously produces the event stream with a high temporal resolution, discarding redundant visual information and bringing new possibilities for moving object detection. Nevertheless, the existing object detectors cannot make the most of the spatial-temporal asynchronous nature and high temporal resolution of the event stream. For one thing, existing methods fail to consider objects with different velocities relative to the event camera’s motion, resulting from the global synchronized time window with the whole spatial slice. For another, most of the existing methods rely on heavy models and boost the detection performance with low frame rates, failing to utilize the high temporal resolution characteristic of the event stream. In this work, we propose a motion robust and high-speed detection pipeline which better leverages the event data. First, we design an event stream representation called Temporal Active Focus (TAF), which efficiently utilizes the spatial-temporal asynchronous event stream, constructing event tensors robust to object motions. Then, we propose a module called the Bifurcated Folding Module (BFM), which encodes the rich temporal information in the TAF tensor at the input layer of the detector. Following this, we design a high-speed lightweight detector called Agile Event Detector (AED) plus a simple but effective data augmentation method, to enhance the detection accuracy and reduce the model’s parameter. Experiments on two typical real-scene event camera object detection datasets show that our method is competitive in terms of accuracy, efficiency, and the number of parameters. By classifying objects into multiple motion levels based on the optical flow density metric, we further illustrated the robustness of our method for objects with different velocities relative to the camera. The codes and trained models are available at https://github.com/HarmoniaLeo/FRLW-EvD.